Enterprises are in a new phase of AI adoption as AI agents transition from answering questions, reasoning, utilizing tools, and accessing data to taking action. But greater autonomy brings with it an inherent conundrum: If an agent discovers a means to achieve its objective that wasn’t originally planned by the agent designers, how does it handle that?

There is a recent case of autonomous AI agents that gives a warning. According to agents, they could access external services, such as Hugging Face, even if direct Internet access was not available, because they discovered an “unintended path” through a shared infrastructure to go to external services. More importantly, agents that didn’t know each other were able to coordinate without explicit knowledge of other agents. The lesson is more general than the incident: the more autonomous the agent, the more it is possible to let an agent do, and the more it poses an engineering problem to get it to do.

A New Enterprise Security Vulnerability in Agentic System

In the case of traditional software, it runs within a specified application and infrastructure. Agentic systems add a standalone decision-making layer between intent and action. An agent can comprehend a goal, choose tools, gather information, call APIs, run code, communicate with other agents and respond accordingly to what it sees.

This opens new opportunities for the emergence of unwanted actions. Agent credentials can be used inappropriately over a network. Capabilities may be exposed through the tool and MCP interface, which may be more extensive than needed for a given task. Communication and shared context and memory can result in cascading privileges and expose sensitive information when communicated between agents. The potential blast radius can be further increased if code can be executed and if ambient credentials are expanded.

There is also another less obvious risk that an agent can meet the measure but not the intent of the measure. An agent that is asked to do something to make software better might not necessarily correct defects but turn down failure signals. One who’s responsible for decreasing build time may skip quality gates. An agent who is tasked to close observations of a security problem may choose to mark a problem as resolved even if they do not take any steps to resolve the problem. In each, the stated goal might seem to be successful, but the business goal is not.

From Guardrails to Containment

That’s where enterprise AI requires another attitude towards security.

Traditional guardrails are based on a fairly linear process: a user gives instruction, a model gives a response, and controls tell what the model can and cannot say or do. This is not the case with agentic systems. They can involve goals, reasoning, tools, APIs, data, actions, and other agents, as well as the order in which they are executed, which can vary dynamically.

Guardrails try to ensure that the system does what it’s supposed to do. The assumption of containment is the opposite: The system is allowed to behave in unforeseen ways, and the danger to the individuals in the system should be kept under control.

Containment isn’t just about making more restrictions on the model. It requires clear limits on who and what can be detected, who has what permission, what information is accessible, what tools can be used, which network can be accessed, how things can be acted out and who/what is indulged in human oversight. For example, a code-review agent can inspect the repository and open a pull request without merging code, accessing production databases or get infrastructure credentials. It shouldn’t be permitted to blindly trust another agent when the first one does.

This also alters the way authorization is going to work. With traditional role-based access, the question is ‘WHO ARE YOU?’. Authorizations for agents should supplement the questions, “what task are you performing? In what context? “What is your special permission to do?” An agent could be granted access to one repository, and be allowed to create pull requests, but not merge them, deploy them to production, or access production secrets.

Design for Controlled Autonomy

With enterprises, it is therefore important to plan containment from the beginning of the development process and not try to implement it once the product is in use. All production agents must have a valid ID, specific roles, access to specific tools, restricted logins, authorized access to tools, and a history of activities.

In addition, there should be human approval for high impact actions, based on risk. Just as crucial, the system should react whenever an agent misbehaves, either by blocking access, keeping an agent off the job or removing its permissions, or by canceling a job.

The goal isn’t to take away independence. It’s intended to be predictable to run in an enterprise setting.

Happiest Minds Perspective: Capability with Control

The vision of Happiest Minds is to make “Capability with Control” the universal perspective in learning and working.

We believe enterprise agentic engineering needs to have a deterministic control plane for autonomous execution at Happiest Minds. Capability and governance are not two distinct requirements, but together they form a co-requirement to put agents into production.

The model should not be termed as the last line of defense.

The surrounding architecture should execute policy at runtime, for e.g. with network isolation, credential vault, task-based authorization, action limitations, observability and human approval gates.

This shifts the design principle from just asking what an agent can accomplish to defining what it is permitted to acquire at each workflow stage.

This leads to a more useful form of enterprise independence: agents are given sufficient autonomy to engage in useful actions, but the organization can still see what they are doing, restrain them if they do something it doesn’t like, and recover from the actions if they do something they doesn’t like.

The Path to Enterprise-Ready Agents

Agent containment will be a requirement for architecture as enterprises transition from experimenting to production. Leaders will have to have answers to some fundamental questions: Does each production agent have an individual identity? Are permissions specific to tasks? Are credentials isolated? Are access privileges to the network explicitly controlled? Is it possible to have human approval for high-risk actions? Will the organization be able to piece back together “what has an agent done” and can it intervene while it is doing it?

If those answers are murky, then it is not a ‘governance’ issue. It poses an element of containment risk.

So, the next wave of enterprise AI is going to be characterized by more than autonomy: it’s going to be characterized by making the autonomy safe, observable, bounded, and recoverable.

Capability + Autonomy + Deterministic Control: The Enterprise-ready Agent Architecture.

The post The Agent Containment Problem: Making Enterprise AI Safely Autonomous first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

Why the future of IT operations is about AI agents working together.

Introduction: The Limits of Isolated Automation

For decades, IT operations teams pursued automating processes and reduce the manual effort, speeding up response time, enhancing service reliability and user experience.

From scenes to runbooks to workflow engines and AIOps platforms, the goal is to automate repetitive operational activities while keeping control.

AI is introducing a new generation of operational capabilities. AI systems can summarize incidents, correlate alerts, recommend remediation actions and even execute predefined workflow system. However, many organizations learn that just integrating AI assistants does not fundamentally change their operations. The reason is simple – most operational problems do not exist within a single technology domain.

A service outage can simultaneously involve application code, server, networking, storage, databases, and IT service management processes. Resolving these concerns needs coordination across multiple tools, data sources, and teams.

This is where the next evolution of IT operations emerges.

Multi-agent orchestration

Rather than relying on a single AI assistant attempting to understand everything, organizations are beginning to deploy specialized agents that collaborate to achieve operational outcomes. The orchestration layer that coordinates these agents is increasingly becoming the new control plane for IT operations.

Why Single-Agent AI Hits a Ceiling

The first generation of AI-powered operations largely follows a simple pattern:

This model works well in ITOps for:

• Incident summarization
• Ticket classification
• Knowledge retrieval
• Log analysis
• Report generation

However, operational reality presents far more complex scenarios. Consider a business-critical application experiencing a drop in performance.

Keeping in mind the root cause may need:

Determining the root cause may require:

• Monitoring metric data analysis
• Application dependency mapping
• Network diagnostics
• Server & storage capacity review
• Database diagnostics
• Recent change analysis
• Service ownership identification
• Incident history correlation

A single AI system involved in the management of all the above domains usually faces multiple challenges:

Information Overload: Operational environments generate enormous volumes of telemetry and context.

Domain Complexity: Network troubleshooting differs significantly from database, for example.

Security Boundaries: Different systems often require different permissions and governance controls.

Context Management: Streamline sufficient context across multiple areas becomes increasingly difficult.

As environments is more distributed and interconnected, the organizations need a more modular approach.

Enter Multi-Agent Operations

Rather than a single AI attempting to perform everything, multi-agent architecture breakdown responsibilities between specialized agents.

Each agent is responsible for a specific operational domain while partnering with others to accomplish a wider goal.

Few examples are:

Observability Agent: Monitors telemetry data and recognizes anomalies.

Topology agent: Comprehends dependencies and service relationships.

Change Intelligence Agent: Analyzes recent changes and deployment activity.

Infrastructure Agent: Manages compute, storage, and virtualization resources.

Network Agent: Performs routing, connectivity, and performance diagnostics.

ITSM Agent: Maintains operational workflows, incidents, and change records.

Security Agent: Evaluates risk, compliance, and access requirements.

Each agent develops deep expertise in its domain instead of acting as a generalist across all scenarios. This mirrors how ITOps teams typically operate.

But specialization alone is not enough; these agents must also coordinate effectively.

The Orchestration Layer: The New ITOps Control Plane

Agents are not the true innovation themselves. It is the orchestration layer that coordinates well with them. As Kubernetes turns to be the control plane for containerized applications then multi-agent orchestration emerges as the control plane for AI-driven operations.

Its responsibilities include:

• Coordinating workflows
• Managing dependencies
• Routing tasks
• Maintaining operational context
• Enforcing policies
• Tracking execution state
• Verifying outcomes
• Escalating exceptions

Instead of focusing on individual tasks, the orchestration layer focuses on outcomes. A modern orchestration workflow portrays:

The orchestrator decides:

• Which agents should participate
• In what sequence
• With what permissions
• Using what context
• Under which policies

The result is a coordinated operational workflow rather than isolated automation.

A Real-World Example: Service Degradation

Imagine a customer-facing application experiencing intermittent performance issues.

Traditionally, multiple teams may become involved:

• Service Desk and Monitoring teams
• Server team
• Network team
• Database team
• Application support team

The process often includes numerous handoffs, duplicated investigations, and delayed resolution.

A multi-agent workflow could operate differently:

The workflow becomes outcome-driven rather than team-driven.

Operational Context: The Fuel for Agentic Operations

One of the most ignored requirements in multi-agent systems is operational context. Agents can only be as good as the data available to them.

A robust operational context layer typically includes:

Telemetry: Logs, metrics, traces, events and user experience information.

Service Topology: Dependency between applications, infrastructure, and business services.

Change History: Deployment, patches, configuration change, and maintenance activities.

Incident Information: Prior incidents, known issues, and fixes.

Operational Policies: Risk classifications, approval, and limitations on execution.

Without context, agents become sophisticated automation tools. With context, they become reasoning systems capable of making informed decisions.

This operational context layer becomes a shared source of truth that all agents can reference.

Governance Must Be Built into the Workflows

One common misconception about agentic operations is that autonomy means removing control. The opposite is true. As autonomy increases, governance becomes more important.

Every operational workflow should include:

Policy Evaluation: Can the action be executed safely?

Confidence Assessment: How certain is the diagnosis?

Risk Analysis: What is the potential blast radius?

Approval Controls: Does human authorization remain necessary?

Auditability: Can each decision be explained?

Rollback: Can this action be reversed if necessary?

Successful applications of AI do not begin with autonomy. They begin with tightly governed workflows and gradually expand autonomy based on demonstrated reliability.

Designing for Human Collaboration

Despite the rise of autonomous systems, humans remain essential. The future operating model is not human versus AI. It is human plus AI. This results in operational teams spending more time on:

• Policy definition
• Exception handling
• Strategic decisions
• Architecture improvements
• Governance oversight

Meanwhile, agents handle:

• Monitoring
• Correlation
• Investigation
• Execution
• Verification
• Documentation

Human expertise moves up the value chain. The objective is not fewer people. The objective is fewer repetitive tasks.

Five Principles for Successful Multi-Agent ITOps

Organizations pursuing multi-agent operations should follow several key principles.

1. Start with the Simplest Architecture

Not every workflow requires multiple agents. Only use orchestration when specialization adds clear value

2. Define Clear Responsibilities

Each agent needs an operational role. Avoid overlapping responsibilities.

3. Prioritize Context Before Autonomy

Invest in telemetry, topology, and operational knowledge before expanding autonomous actions.

4. Build Governance into Every Workflow

Policies should guide actions before execution, not after.

5. Measure Outcomes, Not Activity

Success should be measured by:

• Reduced MTTR
• Improved availability
• Faster recovery
• Reduced operational effort
• Better service reliability

The goal is operational outcomes, not agent utilization.

Conclusion: The Future Is Coordinated Intelligence

The next phase in AI for IT Operations is not driven by a single all, but by intelligence working in concert.

With the growing complexity of IT ecosystems, organizations will need special agents that will be able to cooperate across operational domains.The orchestration layer coordinating them is becoming the new control plane for modern IT operations.

Successful organizations won’t be those who employ more agents, but those who manage them efficiently. The future of ITOps, competitive advantage will be achieved by the AI systems that will reason, coordinate, govern, execute and validate the outcome together. That is the promise of multi-agent orchestration.

The post Multi-Agent Orchestration as the New ITOps Control Plane first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

A few years earlier, the supply chain conversations were mostly about sourcing, logistics, delivery timelines and costs. Cybersecurity was part of discussion, but it was barely the starting point.

That has altered.

Today, when speaking with the customers across the industries, one concern pops up consistently such as how do we ensure our business remains secure when so much relies on external partners?

It is a valid question.

Modern businesses are no longer operating within clearly defined boundaries. Every organization relies on cloud providers, software vendors, managed service partners, logistics, financial establishments, consultants and hundreds of other third parties. Each of these connections supports the business grow faster but every one of them also leads to cyber risks. When geopolitical tensions enter the picture, risks grow even further

Today, organizations are not judged anymore solely by the strength of their own cybersecurity controls. It increasingly depends on the resilience on the security position of the partners they trust.

In plenty of ways, cybersecurity is the shared responsibility across the entire supply chain.

The New Reality of Supply Chain Cyber Risk

Current industry studies put forth that more than 60% of organizations have experienced a cybersecurity incident coming from a third party or supply chain partner. Even more concerning, security leaders consistently rank third party risk among their top three cybersecurity concerns, ahead of many traditional threats.

This is not surprising when we consider the scalability of the modern digital ecosystem. A single enterprise may now build relationships with hundreds or even thousands of suppliers, software vendors, cloud providers, data processors and managed service partners.

Each connection improves efficiency and expands the attack surface. Today, attackers increasingly view suppliers as the most effective way in larger organizations. Why is attacking one heavily protected enterprise when compromising a trusted partner can potentially give

access to many?

Modern organizations operate within interconnected digital ecosystems where a single supplier compromise can create cascading impacts across multiple industries.

A Different Type of Supply Chain Challenge

The geopolitical scenario in 2026 is more fragmented than many businesses anticipated. Trade policies are evolving. Governments are coming up with stricter regulations around data sovereignty and strong infrastructure. Regional conflicts are impacting technology partnerships, manufacturing decisions and digital services in ways that few organizations expected just a few years ago.

While these developments are often seen through an economic/operational lens, there is other side that deserves equal attention. Cybersecurity!

A disrupted supply chain no longer needs stopping trucks/shutting down ports. In many cases, disrupting a software provider, compromising a supplier’s network/targeting a shared technology platform can create a similar business impact.

The effects spread quickly because today’s supply chains are deeply connected through digital systems.

One weak link can affect hundreds or sometimes thousands of organizations.

What makes these incidents particularly important is that many affected organizations were not directly broken through weaknesses in their own systems. They were affected because they trusted a partner.

That distinction basically changes how organizations must think about cyber resilience.

Trade Wars Are Creating New Cyber & Supply Chain Risks

One more challenge is that reshaping supply chain resilience is the growing wave of trade conflicts and economic fragmentation across major global economies.

For decades, organizations optimized supply chains for cost, efficiency and speed. Resilience today is equally important. The tariffs, export controls, sanctions, and shifting trade alliances forces companies to question where they source their products, store their data, produce goods, and provide services. Recent research reports that the instability of tariffs and geopolitical concerns are biggest challenges that global supply chain leaders have to face, encouraging businesses to diversify and redesign operating model.

What makes this particularly challenging is that trade disruptions are not limited to physical goods alone. The results increasingly extend into technology ecosystems, cloud services, software dependencies, semiconductors and critical digital base. Many studies direct to an expanding trend of geopolitical fragmentation, where countries are prioritizing strategic resilience and economic security over traditional globalization models.

I’ve observed that many organizations are responding by diversifying suppliers across regions, moving toward “multi country” sourcing strategies, and investing in regional operating models. While these approaches reduce concentration risk, they also introduce new cybersecurity challenges:

• More suppliers to assess and monitor.
• More digital connections and integrations.
• More regulatory requirements across jurisdictions.
• Greater complexity in managing third-party cyber risk.
• Increased exposure to varied cybersecurity levels.

As a result, efforts to reduce geopolitical risk can unintentionally increase cyber risk if governance and oversight do not change at the same speed

Today’s supply chain leaders must therefore balance three competing priorities:

1. Operational Efficiency
2. Geopolitical Resiliance
3. Cybersecurity Assurance

Organizations that pay attention to only one of these dimensions risk creating vulnerabilities in the other two

Cyber Risk Doesn’t Stop at Your Network

One of the biggest misconceptions I still come across is the belief that strong internal security is enough. Of course, organizations need robust control within their own environment. But no business functions in isolation anymore.

If a critical vendor experiences a cyber incident, the impact can extend well beyond that organization. Production schedules can slip. Customer services may become unavailable. Sensitive data may be exposed. Compliance obligations can suddenly become much more complex.

In other words, your cyber resilience is increasingly tied to the resilience of your ecosystem.

That’s why supply chain security has moved beyond being an IT discussion. It has become a business risk discussion.

If a critical vendor experiences a cyber incident, the outcome can grow well beyond that organization. An example is when the Jagaur Land Rover was hacked back in August 2025 and it was an absolute nightmare. They are one of the biggest car manufacturers in the UK and their entire global production got interrupted for weeks and it left more than ~5000 of their suppliers completely in the lurch.

The financial hit was staggering. JLR had to eat about ~£196 million in direct costs just to deal with the cyberattack and watch their revenues tank by almost 25%. But honestly, the ripple effect was even worse. The wider UK economy took a massive £1.9 billion punch to the gut just from the downstream disruption.

Watching that whole disaster unfolds really changed how I view the economics of cybersecurity. It drove home a few hard truths for me:

• You can’t just guess the risk: It is important to start quantifying these threats and running realistic, worst-case scenarios. If not modeled how bad an attack could be, then no one is ever going to invest enough in resilience to survive it.
• It is way too interconnected: The JLR attack proved how risky our supply chains are.

Why Traditional Vendor Assessments Are Losing Their Value

For years, vendor risk management followed a familiar process.

• A supplier completed a security questionnaire.
• Relevant certifications were reviewed.
• An assessment was performed before onboarding.
• The process was repeated the following year.

While this approach helped establish governance, it no longer reflects how quickly cyber risks evolve.

A supplier that met every security requirement last year could face new vulnerabilities today. Software dependencies change, employees change, infrastructure changes. Threat actors continuously look for new opportunities.

The risk profile of a business is never static.

That is why many organizations are shifting towards continuous visibility instead of depending only on annual assessments. The goal isn’t to create more paperwork. It’s to have a clear understanding of where risk exists before it turns into a business problem.

Governance Has to Keep Pace with Technology

As someone working closely with Governance, Risk and Compliance (GRC), it is seen how the role of governance has evolved.

Previously, governance was often seen as a compliance exercise. Success was evaluated by completed audits, documented policies and regulatory checklists.

Today, governance needs to help leadership answer questions that have direct business impact.

• Which suppliers are most critical of our operations?
• Do we know which third parties have access to our sensitive data?
• How quickly can we identify a cyber incident affecting one of our partners?
• Are our contractual obligations aligned with today’s cyber risks?
• Can we continue serving customers if one of our critical service providers becomes unavailable?

These questions go beyond compliance. They impact operational continuity, customer trust and business performance.

That’s why it is believed that GRC is becoming one of the most strategic functions within cybersecurity.

AI is Accelerating Both Defense and Adversaries

One major growth that deserves particular attention is the pace at which AI is compressing the cyber kill chain.

Activities that once needed days/weeks of reconnaissance can increasingly be done in hours. Adversaries can now automate vulnerability discovery, generate highly convincing phishing content and customize social engineering campaigns at an unanticipated scale.

At the same time, security teams are managing AI powered analytics to detect anomalous behavior, correlate threat intelligence and lower response time.

For the first time in cybersecurity history, both defenders and adversaries are taking advantages from the same transformative technology simultaneously.

The organizations that succeed are those that combine AI driven capabilities with strong governance, human oversight and imperative risk management practices.

More Suppliers, More Digital Connections, More Risk

Plenty of organizations are redesigning their supply chains to lower dependency on a single geography or vendor. It’s a logical business decision.

However, every additional supplier also creates another digital connection. Another integration, Another user account, Another API, Another application. Individually, these may appear manageable. Together, they create an environment that is far more tough to track and govern.

This isn’t an argument against growing supplier ecosystems. It’s simply a reminder that growth must be accompanied by better visibility.

Without proper vision, risk accumulates quietly until an incident brings it to the surface.

A Boardroom Priority, Not Just a Security Priority

Cybersecurity has earned the place in boardroom discussions and so. The conversations seen today are very different from those asked few years back. Leadership teams are asking practical business questions.

• How exposed are we?
• Which supplier poses the greatest operational risk?
• What happens if one of our key partners is unavailable tomorrow?
• How quickly can we recover?

These are not technical questions. These are related business questions that require technology, governance and leadership to work together.

The transition is encouraging as it shifts cybersecurity away from being seen as a cost center and positions it as a driving force of business continuity and customer confidence.

The Regulatory Push Towards Supply Chain Accountability

Another major driver of change is regulation.

Frameworks such as DORA (Digital Operational Resilience Act) in the European Union, along with evolving requirements from NIS2, GDPR, and sector-specific regulations, are placing greater emphasis on third-party risk management than ever before.

Regulators increasingly expect organizations to understand not only their own risks but also the risks posed by critical suppliers, technology partners, and outsourced service providers.

The message from regulators has become unmistakably clear. Cyber resilience is no longer limited to organizational boundaries. Accountability extends across the broader digital ecosystem.

For boards and executive leadership teams, this represents a significant shift from periodic compliance reviews toward continuous monitoring and operational resilience.

Looking Ahead

If the last few years have taught us anything, it’s that uncertainty is becoming part of normal business operations.

Geopolitical developments will continue to evolve. Regulations will become more demanding. Supply chains will become more distributed. Digital ecosystems will continue to expand. Organizations cannot control these external factors. What they can control is how prepared they are.

Supply chains will continue to evolve. Organizations will adopt more cloud services, integrate more AI-driven solutions, and expand partnerships across multiple regions.

The digital ecosystem will become even more connected than it is today.

That also means cyber risk will continue to extend beyond organizational boundaries.

For years, cybersecurity has focused on building stronger defenses. While that remains important, the next phase is about building stronger relationships between organizations, their suppliers, technology partners, and leadership teams.

Resilience is built on visibility. It is strengthened through governance. And it is sustained through collaboration.

At Happiest Minds, we’ve seen this shift firsthand while working with enterprises navigating increasingly complex digital environments. The organizations making the greatest progress aren’t chasing every new security trend. They’re taking a measured approach, aligning cybersecurity with business objectives, embedding governance into decision making, and continuously strengthening trust across their partner ecosystem.

That, in my view, is where supply chain resilience is headed.

Not as a technology initiative. Not as a compliance requirement. But as a business capability that enables organizations to adapt with confidence, even in uncertain times.

The future of cybersecurity will not be defined solely by stronger firewalls, better monitoring tools, or larger security budgets. It will be defined by an organization’s ability to understand, manage, and strengthen the ecosystem it depends upon.

In a world where every supplier, technology platform, cloud service, and business partner forms part of a connected digital value chain, resilience becomes a collective capability rather than an organizational one.

The most successful organizations of the next decade will not necessarily be those that seek to eliminate every risk. They will be those that continuously identify critical dependencies, maintain visibility across their ecosystem, and respond to disruption with speed and confidence.

Because in 2026 and beyond, supply chain resilience is no longer merely a cybersecurity objective. It is a strategic business imperative

The post Supply Chain Resilience in 2026: Navigating Risks Amid Geopolitical Fragmentation first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

For the better part of a decade, retail’s favorite piece of AI jargon was “next best offer.” A model would look at what you’d bought, what you’d browsed, and what people like you tended to buy next, and it would surface a recommendation. Someone still had to act on it: a merchandiser adjusted an allocation, a marketer approved a campaign, a store associate decided whether to honor a price match. Intelligence lived in the suggestion. The decision, and the accountability that came with it, stayed human.

That boundary is the thing moving in 2026. Agentic AI in retail doesn’t stop at “here’s what you should probably do.” It monitors a signal, weighs the options, and acts, reserving inventory, re-routing a shipment, adjusting a price, or resolving a return, inside boundaries a person has defined in advance. This shift from recommendation-driven retail AI to AI-powered decision-making is a fair summary of what’s changed: the system is no longer just informing a choice; it’s making a bounded one.

It’s worth being precise about what’s real here versus what’s still slide-deck ambition, because retail has seen AI promises to move faster than the data, processes, and technology infrastructure underneath them. The real opportunity for AI agents in retail is not simply to automate more tasks, but to connect signals to decisions and actions across retail operations.

Where Agentic AI Is Already Delivering in Retail

This is best illustrated not by the creation of a flagship product but through the distribution of narrower, properly enclosed agents performing distinct tasks. The attempt by Microsoft to enter retail via Dynamics 365 is a great illustration of the category, since it shows how the agentic AI is divided up according to roles and not marketed as an omniscient brain, including Catalog Enrichment Agent, Personalized Shopping Agent, and Supplier Communications Agent. Around that platform, a layer of partner-built agents has shown up doing equally specific work, from voice-first agents handling point-of-sale tasks on the floor to clienteling agents that prep a store associate before a customer walks in, and “commerce companion” agents that triage store operations questions so people don’t have to dig through policy documents mid-shift.

The pattern across these use cases is the same: pick a decision that is frequent, bounded, and currently a bottleneck, and let an agent own more of it end to end, rather than trying to automate judgment broadly. AI agents in retail that assess stock and reserve product across channels can be easier to operationalize than agents attempting full autonomous merchandising, because the former typically have clearer success criteria and more measurable outcomes.

Customer service is where the numbers are often most concrete, because it’s the area with the longest deployment history and the clearest before-and-after metrics. AI agents handling disputes, refunds, and shipping issues are increasingly being deployed in large e-commerce and contact-center operations, with containment, resolution time, and self-service completion among the metrics retailers are using to measure impact. That’s a meaningful shift in cost structure, and it’s why customer service and returns handling are often among the first places agentic AI earns its budget in a retail transformation program, well before anyone touches pricing or merchandising.

The Gap Between Agentic AI Adoption and Business Impact

Here’s the less comfortable number: industry adoption is moving quickly, but measurable business impact is still lagging. Deloitte’s 2026 survey of retail and CPG executives found that 75% consider AI a top strategic priority, while only 16.5% can quantify a return on their AI investments. Almost every retailer has AI in retail somewhere in the stack. Far fewer can point to a measurable P&L impact. That gap is the actual story of 2026, more than any individual agent capability. It’s the difference between installing a technology and re-architecting a decision process around it.

The failure mode is familiar to anyone who’s sat through a transformation program: an agent gets deployed against a process that was never redesigned to use it. A returns agent bolted onto a workflow still built for a human reviewer doesn’t remove the bottleneck; it just moves it. A personalization agent layered on top of a product catalog with inconsistent, incomplete data will make confident, well-reasoned bad recommendations, which is worse than making no recommendation at all. Agentic AI adoption amplifies the quality of the decision-making infrastructure it’s dropped into, good or bad, and most retailers’ infrastructure was built for reporting, not for autonomous action.

What Shoppers Really Want From AI Shopping Agents

The other useful corrective comes from consumers themselves, not from vendors. Walmart’s 2025 Retail Rewired research found that 27% of respondents preferred AI-powered shopping suggestions, nearly on par with the 24% who preferred influencer recommendations, which quietly says something about how far AI credibility has come. But it also found that what earns that trust isn’t novelty: shoppers valued practical utility such as price and shipping comparisons, price-drop alerts, and filtering based on past purchases far more than anything resembling a “smart assistant” personality. AI is winning on helpfulness, not hype.

The same research found that 46% of consumers would be unlikely to let an AI shopping agent handle an entire shopping trip independently, and that trust in AI drops for higher-value or emotionally significant purchases even among people comfortable letting it handle routine household restocking. Most people, given the option, still type into a search bar rather than delegate to an agent. That’s not a sign agentic AI is failing to land; it’s a sign the technology is landing exactly where it’s earned trust so far: narrow, reversible, low-stakes decisions, with humans still holding the wheel on anything that matters more.

That’s a healthy state for the category to be in, not a disappointing one. Retailers chasing full autonomy on the customer-facing side, before earning trust on the boring back-office decisions, are optimizing for a demo rather than for adoption.

What Makes Agentic AI Work in Retail

Looked at across supply chain, customer service, and merchandising, the retailers getting real return from agentic AI tend to share three things that have very little to do with model quality.

They pick decisions with a fast, measurable feedback loop, in other words, inventory reservations, return approvals, supplier confirmations, and similar workflows where an agent’s mistake is cheap and quickly visible, rather than decisions where being wrong takes a quarter to notice. They fix the data and process underneath the agent before adding the agent, because an autonomous system built on inconsistent product data or an undocumented policy exception doesn’t route around the mess; it confidently acts on it. And they draw an explicit line between what the agent decides alone and what it escalates, not as a temporary training-wheels measure, but as a permanent design choice, because the point of agentic AI implementation in a business with real customers and real inventory was never to remove human judgment from consequential decisions. It was to remove human effort from the repetitive ones so that judgment gets spent where it actually matters.

For retailers and CPG companies, that means the opportunity is not simply to add more agents. It is to connect the signals already sitting across demand, inventory, promotions, stores, supply chains, and shopper behavior to decisions that can actually be executed. The closer the organization can get from signal to decision to action and measure the outcome, the more meaningful the value of agentic AI becomes.

That’s the more grounded way to read where retail is in 2026. Agentic AI hasn’t replaced the decision-maker. It’s replaced the decision, the frequent, boring, high-volume kind while pushing the interesting ones further up the chain to the people best equipped to make them. The next best offer told you what to consider. The next best decision, done well, is the one you never had to think about at all, and the handful you still get to make yourself.

See Agentic AI in Action at GroceryShop 2026

Moving from next best offer to next best decision means translating insights into actions. At the GroceryShop 2026, Happiest Minds will discuss the role that AI can play in connecting signals related to demand, inventory, supply chain, store, and shopping behavior to make better and faster decisions. From Agentic Commerce and demand sensing to autonomous supply chain and intelligent store planning, the Happiest Minds and FieldAssist teams will showcase practical applications of AI for Retail and CPG.

September 22–24, 2026 | Las Vegas | Booth 1035

Learn more about GroceryShop 2026

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Why successful organizations are moving from AI assistance to agentic operations, one maturity stage at a time.

Introduction: The Next Evolution of IT Operations

AI is becoming an integral part of the IT operations toolkit and organizations have already integrated AI-powered copilots that summarize incidents, produce reports, or troubleshoot issues. These capabilities validate productivity improvement but represent only the beginning of a fundamental shift. The next wave of innovation will not be about AI giving better answers. It is about AI helping organizations move from assisted operations to supervised action and eventually to bounded autonomy.

The key transition is from AI that recommends or suggests actions and awaits human response to AI that performs them. In the IT operations context, there are agents that not only identify anomalies, but also diagnose issues, escalate problems, remediate, and validate outcome often without manual intervention. The essential capabilities anticipated from agentic AI in IT Ops include:

• Contextual reasoning – going beyond simple rules or threshold matching
• Executing multiple steps across various systems rather than just single responses
• Self-learning and correction, allowing the agent to adjust if initial actions fail or if the environment changes
• Tool integration – agents interact with APIs, execute scripts, consult CMDBs, and initiate ITSM workflows as part of their decision-making process

This shift is creating a new operating model for IT operations. However, successful organizations are discovering that autonomy is not a destination that can be reached overnight. It must be earned through a structured maturity journey. The most effective approach can be viewed as three distinct stages of AI adoption in IT operations.

AI-Assisted to Agentic IT Operations Maturity Model

The three maturity stages reflect progression from recommendation-oriented AI systems to ones that diagnose, plan, act within policy, and verify outcomes.

Stage 1: AI-Assisted Operations

AI helps humans work faster

Most organizations are at the first stage today. In this model, AI is an intelligent support that analyzes data, summarizes information, detects patterns, and recommends actions, but humans are making decisions and execution. Examples are:

• Incident summarization
• Alert classification and prioritization
• Root-cause suggestions

• Knowledge article retrieval
• Ticket enrichment and routing
• Automated operational reporting

With an alert, AI may analyze logs, correlate events, and recommend a likely threat. However, an engineer still investigates the trigger, determines the response, and runs remediation activities.

The value of this stage is straightforward:

• Faster incident triage
• Reduced manual effort
• Improved knowledge access
• Better decision support
• Increased operational consistency

Plenty of organizations achieve measurable productivity improvement without altering their operating model. However, AI-assisted operations are still suffering from one basic challenge: human is the bottleneck for action. Even after AI identifies the correct resolution, engineers have to review recommendations, navigate multiple tools, execute changes, and validate outcomes manually to move beyond efficiency gains, organizations must enter the second stage.

Stage 2: Supervised Agentic Operations

AI begins taking actions with human approval

The second stage introduces agentic behavior.

Unlike traditional AI, which provide recommendations, agentic systems are pursuing their own end goals. They perform multi-step workflows, interact with operational tools, and take action under defined controls. The fundamental difference is that AI is no longer limited to analysis. It can:

• Gather operational context
• Correlate telemetry across systems
• Create remediation plans
• Execute approved runbooks
• Validate results
• Document actions automatically

Take an instance of recurring backup failure in a traditional environment, where an engineer receives an alert, investigates logs, detects a stale lock, clears the lock, reruns the backup, validates success and updates the ticket. In a supervised agentic model, an agent can:

1. Detect the failure
2. Analyze logs and identify the cause
3. Recommend clearing the lock
4. Present the plan for approval
5. Execute the approved action
6. Verify successful completion
7. Update operational records

The engineer becomes a reviewer rather than an executor.

This stage shows significant improvements in:

• Mean Time to Resolution (MTTR)
• Operational consistency
• Response speed
• Knowledge capture
• Staff productivity

Above all, organizations begin to build trust in AI-driven operational workflows while maintaining human oversight

Stage 3: Bounded Autonomy

AI executes within defined boundaries

The third stage represents the future vision of IT operations. In this model, AI systems operate with bounded autonomy. They can act without waiting for human approval, but only within predefined policies, confidence thresholds, and governance controls. Autonomy does not mean unrestricted execution.

Before any action occurs, the system evaluates:

• Risk level
• Confidence score
• Change impact
• Policy compliance
• Rollback availability
• Operational constraints

High-risk activities may still require human approval and actions. Low-risk, well-understood activities can proceed autonomously.

Example use cases include:

Autonomous Resource Optimization

An agent identifies underutilized cloud resources and executes approved optimization actions within defined thresholds.

Automated Service Recovery

An agent identifies a failed service, restarts the service, validates recovery, and process operational records automatically.

Self-Healing Infrastructure

An agent identifies known configuration drift and restores the approved configuration without human intervention.

Intelligent Capacity Management

An agent forecasts resource exhaustion and initiates approved scaling actions before service degradation occurs.

The objective is not to remove humans from operations.

Instead, humans focus on:

• Strategy
• Governance
• Exception handling
• Architecture decisions
• Policy management

Routine operational activities become increasingly automated.

The Foundation: Operational Context Matters More Than AI

One of the biggest misconceptions surrounding autonomous operations is that success depends primarily on selecting the right AI model. Autonomy depends far more on operational context. An AI agent cannot make reliable decisions without understanding:

• Service dependencies
• Infrastructure topology
• Configuration relationships
• Historical incidents
• Change history
• Operational policies
• Business impact

Organizations must establish a trusted operational context layer that combines:

Telemetry

Logs, metrics, traces, events, and observability signals.

Topology

Relationships between applications, services, infrastructure, and dependencies.

Service Context

Ownership information, service maps, operational procedures, and escalation paths.

Historical Knowledge

Previous incidents, changes, known errors, and remediation patterns.

Without reliable context, even advanced AI systems become sophisticated recommendation engines rather than trusted operational agents.

Governance: The Difference Between Automation and Autonomy

As organizations shift towards autonomous operations, governance has become more significant. Every autonomous action should answer these three questions:

Was the action allowed?

Policies must define what an agent can and cannot do.

Was the action explainable?

Organizations need audit trails showing:

• What decision was made
• Why it was made
• What evidence was used
• What actions were taken

Was the outcome verified?

Autonomy without verification creates operational risk.

Every action should be followed by:

• Health validation
• Service verification
• Rollback capability
• Escalation procedures

The most successful organizations implement confidence-based autonomy models where authority expands only as trust and operational evidence increase.

Conclusion: Autonomy Is Earned, Not Deployed

The future of IT operations is not about replacing people with AI. It is about redesigning operations so that there are fewer human involvements, performing repetitive tasks and more time focusing on strategic deliveries. Successful organizations that have adopted AI understand that the journey follows three distinct stages:

1. AI-Assisted Operations – AI informs human decisions.
2. Supervised Operations – AI executes actions with human oversight.
3. Bounded Autonomy – AI operates within trusted policies and governance boundaries.

The organizations that move through these stages deliberately, building operational context, governance controls, and trust along the way, will be best positioned to realize the full potential of agentic IT operations. Automation is not the end goal

The goal is to achieve speedy recovery, reliability, reduced operational burden, and strong digital services. It’s important for organizations to know what stage of AI adoption they are currently in today.

The post The Three Stages of AI Adoption in IT Operations first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

A Look at the U.S. Annuity Market

As annuities evolve into a powerful distribution channel for alternative investments and private-market strategies, carriers face a new challenge; their legacy insurance tool were never designed to provide speed, ecosystem connectivity and product agility this model asks for.

This is where Happiest Minds Insurance-in-a-Box becomes a strategic enabler. It is modern, cloud-native, API first insurance tool that is designed to support carriers launch, distribute, and administer insurance and annuity products quite rapidly in traditional, digital, embedded and ecosystem-driven channels.

Above all, it provides the scalability required to control emerging products such as RILAs, index-linked annuities and alternative-investment-powered retirement solutions without the carriers required to go through expensive core-system transformation.

Key Capabilities for Modern Carriers

Accelerated Product Innovation

Insurance-in-a-Box enables carriers to configure and launch new insurance products through low-code product factories and reusable business components. As product manufacturers introduce innovative annuities with private market exposure, the platform reduces time-to-market from months to weeks.

Ecosystem-Driven Distribution

When distribution takes place through broker-dealers, wealth management firms, fintech platforms, retirement ecosystem, embedded insurance partners or digital marketplaces, the Insurance-in-a-Box gives standardized APIs that streamline onboarding and integration.

Instead of creating custom interfaces for every distribution partner, carriers can establish a scalable digital ecosystem model.

Digital Operations at Scale

Modern annuity products need complicated servicing, sustainability workflows, disclosure, validated compliance, and customer interactions. The platform digitizes policy administration, customer onboarding, servicing journeys, and claims operations while giving a stable experience within channels.

Data, Compliance & Transparency

When regulators increase scrutiny around alternative-investment products, carriers require bigger transparency and governance. Insurance-in-a-Box offers end-to-end data viewing, operational reporting, auditability and configurable workflow maintains to help compliance, suitability evaluation and disclosure requirement.

Future-Ready Integration Architecture

The platform coexists with existing policy administration systems, investment systems, and partner ecosystems. This drives carriers to update incrementally while safeguarding early technology investments. Rather than altering the core, Insurance-in-a-Box acts as an agile digital layer that reveals new business models

Why This Matters Commercially

For carriers seeking growth in a highly competitive retirement and wealth market, the value extends well beyond technology.

Insurance-in-a-Box enables organizations to:

• Launch innovative annuity products faster.
• Expand distribution through digital and partner ecosystems.
• Improve advisor and policyholder experience.
• Reduce onboarding effort for new partners.
• Increase cross-sell and upsell opportunities.
• Support embedded insurance and Insurance-as-a-Distribution-Channel models.
• Create scalable operations without large core modernization programs.
• Build a foundation for AI-driven underwriting, servicing and customer engagement. Significantly, it helps carriers transit distribution from a traditional sales function into a digital growth machine.

The Future Belongs to Distribution-Led Insurers

When private markets find their way into retirement products, the role of insurers is transforming. Carriers are no more simply manufacturers of insurance product, but they are the ecosystem orchestrators, bringing together asset managers, advisors, digital platforms and end customers through intelligent distribution networks.

The winners in this next phase of the insurance industry will not necessarily be those with the largest product portfolios. They will be the carriers that can launch products faster, onboard partners seamlessly, adapt to changing regulations, and deliver superior digital experiences.

At Happiest Minds, we believe that the future of insurance lies at the interaction of product innovation, ecosystem connectivity, digital operations and intelligent automation. Insurance-in-a-box was designed precisely for this future, allowing carriers to shift distribution into a long-term competitive edge and capitalize on upcoming opportunities such as alternative-investment-enabled annuities, embedded insurance and next-gen retirement solutions.

How Leading Carriers Are Redefining Distribution

The evolution of insurance from a traditional product business to a distribution-led ecosystem model is already underway. Several leading insurers and retirement providers have demonstrated how technology, partnerships, and innovative product structures can create competitive advantage.

a) Athene + Apollo: Reimagining Annuities Through Alternative Assets

Athena is one of the fastest-growing annuity providers by controlling Apollo’s alternative investment capabilities. Rather than depending solely on conventional fixed-income portfolios, Athena incorporated private credit and alternative asset approaches into its investment strategies, allowing it to offer competitive retirement solutions while driving attractive returns.

Key Takeaway for Carriers: Success highly depends on combining product manufacturing expertise along with the sophisticated investment and distribution ecosystem.

b) Lincoln Financial and BlackRock Partnership

Lincoln Financial partnered with BlackRock to introduce investment and retirement products that provide broader access to diversified investment strategies. These collaborations are enabling insurers to offer differentiated retirement solutions that go beyond traditional annuity structures.

Key Takeaway for Carriers: Strategic ecosystem partnerships are becoming a critical source of product innovation and market differentiation.

c) Nationwide’s Digital Distribution Strategy

Nationwide has invested heavily in advisor-based digital capabilities, simplifying annuity sales, onboarding, servicing and policy administration. The company has enhanced speed-to-market and elevated advisor engagement by modernizing distribution operations and advisory experiences.

Key Takeaway for Carriers: Digital distribution capabilities often is a powerful differentiator than product attributions alone.

In a conclusion, the future insurance leaders will not be defined alone by the products they manufacture but by the atmosphere they enable. However, annuities become a gateway to alternative investments and embedded insurance rethinks how protection is distributed, carriers need an operating model developed for speed, connectivity and innovation.

The post What Insurance-as-a-Distribution-Channel Means for Carriers first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

Why U.S. Carriers and Distributors Should Care Now: Moving from Product Configuration to Production at AI Speed  

AI is changing the way insurers, distributors, and IMOs innovate, configure, test, and release their products. In an industry where there is constant change to the requirements for the products, the means of distributing the products, and what customers expect from these products, the ability to transition from designing the product to delivering the product is key. It is not necessarily the insurers, distributors, and IMOs with the best-designed products that succeed. Instead, success depends on being able to get innovative products to market quickly. 

For decades, insurance product launches followed a predictable, often painful rhythm like months of manual configuration, siloed testing cycles, and workflow customization that required deep technical expertise and constant back-and-forth between business and IT teams.   

In the process of getting to market, the business environment that necessitated development of that product may already have altered, become narrower, or even been seized by the competition. 

That model is becoming increasingly difficult to sustain. AI is creating a more connected insurance product life cycle, helping organizations move from product configuration to production with greater speed, precision, and control.  

How AI Is Rewiring the Insurance Product Lifecycle  

The real shift happening in insurance technology isn’t simply “AI helps you go faster.” It’s that AI-native platforms are fundamentally changing how the product life cycle works. Instead of treating AI as a bolt-on feature layered over legacy configuration tools, AI-native architectures embed intelligence directly into the core processes of insurance product development, including product design, configuration, testing, and deployment.   

This means: 

• Product configuration becomes a guided, intelligent process rather than a manual, rules-heavy exercise. AI can recommend configurations based on historical patterns, regulatory requirements, and distribution channel needs. This can help carriers manage product variations more efficiently as they adapt to offerings across different markets and insurance distribution channels.  
• Workflow customization adapts dynamically, with AI identifying where existing workflows can be reused, modified, or retired rather than rebuilt from scratch. This creates greater flexibility across the insurance product life cycle while reducing the effort involved in supporting product-specific workflows.  
• Testing and validating moves from reactive checkpoints at the end of the cycle to a continuous, automatic process that detects problems at an earlier stage and eliminates rework. In the case of insurance, this method helps to make validation as close to configuration as possible. 
• Production deployment happens with greater confidence and speed, because of the groundwork, configuration accuracy, workflow integrity, and compliance alignment has already been validated along the way.    

The cumulative effect is a compression of the entire idea-to-production timeline, without sacrificing the governance and accuracy that regulated insurance products require. For carriers and distributors, this creates a more responsive approach to insurance product development, helping them move from product configuration to market-ready offerings with greater consistency.   

Why AI-Native Product Development Matters for Carriers, Distributors, and IMOs    

Three forces make this an urgent conversation rather than a future one. Together, they are increasing the need for AI in insurance product development, particularly as carriers seek to respond faster to changing products, channels, and customer expectations.   

1. Distribution complexity is increasing. Carriers and IMOs are managing more products across more channels and partners than ever before. Manually configuring and testing each variation doesn’t scale. As insurance distribution becomes more complex, AI-assisted product configuration can help teams manage these variations with greater efficiency.   
2. Customer and advisor expectations have shifted. Both end consumers and distribution partners expect product changes, riders, and pricing updates to reflect current market realities and not last quarter’s.   
3. Competitive pressure rewards first movers. In a market where product parity is common, the carrier that can launch, iterate, and personalize fastest captures disproportionate share before competitors catch up.  

Carriers who continue to rely solely on traditional configuration approaches risk falling behind. This is not because their products are worse, but because their time-to-market is slower.   

Engineering AI-Native Insurance Product Development    

Making that shift real, takes more than intent. It takes a delivery model built for it. This is where Happiest Minds’ Generative AI Business Services (GBS) practice comes in, bringing a structured Consult → Build → Validate → Scale approach that takes carriers from strategy to production. Rather than treating AI as a bolt-on to existing platforms, GBS is designed to:   

• Layer intelligence onto current systems by enhancing existing platform investments instead of forcing costly rip-and-replace projects.   
• Build purpose-fit GenAI applications and agentic workflows including reusable “companions” for functions like contracts, customer service, and research, so capability doesn’t have to be built from zero each time. 
• Engineer for governance from day one by embedding compliance, security, and quality checks into the AI itself, so speed and regulatory rigor move together rather than trading off.   
• Draw on a proven technology ecosystem spanning leading LLMs, cloud platforms, and agentic frameworks, so carriers aren’t locked into a single vendor’s road map.  

It’s this combination of a repeatable delivery methodology and deep AI engineering expertise that turns “AI-native” from an industry aspiration into something carriers can actually stand up, govern, and scale.  

How Happiest Minds Is Advancing AI in Insurance    

At Happiest Minds, we’re partnering with insurance carriers, distributors, and IMOs to bring AI-native capabilities into the heart of their product and platform strategy. Our approach to AI in insurance focuses on enhancing existing platform investments while helping organizations modernize product development, configuration, testing, and deployment.  

Our approach focuses on: 

• Accelerating product innovation by embedding AI into configuration and design workflows, reducing the manual effort required to bring new ideas to life.  
• Enhancing existing platforms with AI-driven capabilities layered intelligently onto current systems, avoiding costly rip-and-replace projects.  
• Automating configuration and testing to shrink validation cycles from weeks to days, or days to hours.  
• Customizing workflows at scale so that distribution-specific or product-specific variations can be built and deployed without starting from zero each time.  
• Bringing new capabilities to production faster, with the confidence that comes from AI-assisted validation throughout the process.  

The result is a more agile, more differentiated insurance ecosystem. One where speed and precision are no longer competing priorities. AI-native transformation is a strategic repositioning of how carriers, distributors, and IMOs compete. The organizations that move from product configuration to production at AI speed today will be the ones setting the pace for the industry tomorrow.  

The question isn’t whether AI will reshape insurance product launch and distribution. It’s whether your organization will lead that shift or catch up to it. 

The post How Can AI-Native Platforms Transform Insurance Product Launch & Distribution first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

A clean penetration test on a GenAI application often answers the wrong question, and no report will tell you that. 

The Assurance Gap Nobody is Reporting 

Enterprises have moved generative AI out of the pilot phase faster than they have moved their assurance practices. The assistant that started as a contained chat interface now reads inbound email, queries the CRM, retrieves from a document store, and calls internal APIs on a user’s behalf. In the space of a release cycle, it has stopped being an interface and started being an actor inside the estate.  

The security testing wrapped around it, in most organisations, has not changed at all. The same scope template goes out. The same skilled testers do the same competent work. The report comes back clean, and the programme moves on. 

That report is usually accurate. It is also, increasingly, an answer to a question nobody should have been asking.  

The problem isn’t that security testing has become less rigorous. It is that the thing being tested has changed, while the definition of what needs to be assured has not. 

When a Clean Pentest Report Misses the Real Risk  

For instance, a client recently shared a pentest report for its GenAI assistant. A failed assessment would have been more reassuring. 

Two findings, both low severity, both closed inside a sprint. Leadership signed off. The application was “secure.” So, the next question was what the test had actually covered. 

The testers had probed the web application around the model: – auth flow, API gateway, session handling, the usual injection points in the surrounding stack. Good work, competently done. But the model itself, the thing making decisions, retrieving data, and talking to customers, had been scoped as a black box that returns text. Nobody had asked what it could be talked into doing. Nobody had mapped where it could reach. 

The report wasn’t wrong. It answered its question accurately. The question was the wrong one, and no report tells you that.   

A Pentest Inherits the Mental Model Behind its Scope 

Penetration testing is bounded by design. You scope it, point it at a target, and skilled people try to break in within that boundary.  It remains one of the most useful practices in security, with established methodologies and mature testing practices build around it. The constraint is that a pentest can only be as good as its scope. Most people writing scopes today learned the craft on deterministic systems: same input, same output, trust boundaries drawn at the network and code layers, “input validation” meaning a sanitized form field. Applied to an LLM, that model fails quietly, because an LLM breaks the assumption underneath it.  

A traditional application has a finite set of intended behaviours. An LLM has an effectively unbounded one. You cannot enumerate the inputs, and you cannot fuzz your way to completeness, because the attack surface isn’t the payload; it’s the meaning of the payload. Meaning doesn’t fit in a wordlist.  

This isn’t a fringe concern. Writing for Georgetown’s Center for Security and Emerging Technology in October 2025, Evelyn Yee put it plainly: the threat model is “the key concept around which the red-teaming exercise is constructed,” because it “bounds the scope of the evaluation.” Bound it wrong and everything downstream inherits the error. 

Four Questions a Pentest Scope Never Asks 

Threat modelling asks what a pentest cannot: given how this system is built and connected, what could go wrong, who would want it to, and what would it cost us? 

A)Where does the model get its instructions, and can untrusted content reach that channel?

The moment your assistant summarizes an email, reads a document, or pulls a webpage, an attacker’s text and your system prompt share the same context window. This is indirect prompt injection, a technique named by Greshake and colleagues back in 2023 and it is a structural property of the design, not a payload you scan for. 

EchoLeak is what that looks like in production. Aim Security disclosed it in Microsoft 365 Copilot in June 2025 as CVE-2025-32711: CVSS 9.3, zero-click, no user interaction required. An attacker sends an email. When Copilot later processes it, hidden instructions pull data from the user’s context and exfiltrate it through an auto-fetched image. The exploit chained past Microsoft’s own cross-prompt-injection classifier and its link redaction. Microsoft patched it server-side and found no exploitation in the wild. 

Notice what finding it required: understanding how Copilot assembled context, what it trusted, and which egress paths survived the content security policy. No payload list produces that. 

B)What can the modelactually do?  

A chatbot that answers questions is a contained risk.  The same model, when wired to tools such as database, an API, a message queue, or a code interpreter, becomes a different system. The danger isn’t the model saying something wrong. It is the model being persuaded to take an action it already has permission to take. Your blast radius is whatever you connected it to. 

C)What can the model see that the user cannot? 

In most retrieval setups,  the model holds access far exceeding that of any individual user. That is not a vulnerability in the model. It is an architecture decision nobody made consciously, and no test will report it, because the system is behaving exactly as configured. 

D)What does it leak about itself? 

System prompts, context window contents, training data: exfiltration targets that did not exist in the application it replaced. 

None of these are found by attacking a finished product. They are seen by understanding it. The attack comes second, to validate the model you built. 

The Objection to Threat Modeling: What It Gets Right 

Here is the pushback, usually from someone who has been sold a bad threat model before: threat modelling is what consultancies bill for when they can’t find bugs. It produces a diagram, a spreadsheet, and no proof. A pentest at least tells you something happened. 

That criticism has earned itself. A threat model that doesn’t end in tested hypotheses is a document, not a control, and plenty of them are exactly that. 

The second objection is more practical. Pentests carry a compliance function threat models don’t. Your auditor, your customer’s security questionnaire, and your cyber insurer all want a test report. None of them currently ask for a threat model. Nobody is swapping one for the other, and that is not the argument here.The argument is about sequence and scope, not substitution. Model the system, find where trust is misplaced and where reach exceeds intent, then aim adversarial testing at those hypotheses and put the result in the pentest report your auditor wants. A red team that starts without a threat model is guessing with talent.   

Framework Set the Floor, Not the Finish Line 

OWASP’s GenAI Security Project shipped a new LLM Top 10 on 3 August 2026, alongside a separate Top 10 for Agentic Applications – ASI01 through ASI10 – covering agent goal hijack, tool misuse, and memory poisoning. If you have wired a model to tools, that second list is the one to read first. MITRE ATLAS maps adversary tactics and techniques against AI systems. NIST’s Generative AI Profile, AI 600-1, extends the AI RMF across twelve GenAI-specific risk categories. 

 They matter, so use them. But a checklist run against a system you don’t understand is an organized way to miss the point. Frameworks tell you which categories of things go wrong. Only a threat model tells you which of them apply to the system you actually built, and what they would cost if they did. 

Before You Sign the Next AI Pentest SOW 

Three questions. Put them in writing, then examine the answers closely. Does the scope include the model’s tool and data reach, or only the application around it? 

1. What untrusted content can enter the context window, and did anyone test that path specifically? 
2. What does the tester assume a successful attack looks like: a string that comes back, or an action that executes? 

If the answers are vague, you are not buying assurance. You are buying a document that says you have it. A passing test on the wrong question is worse than no test at all, because it manufactures confidence exactly where scrutiny belongs. 

The post When Passing an LLM Pentest Is the Problem first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

Why fragmented IT is quietly eroding portfolio value, and what unified operating model change

Private equity has gotten very good at underwriting value creation on paper, synergy models, EBITDA bridges, 100-day plans, and add-on roadmaps. Technology, however, is where a surprising amount of that modeled value quietly leaks back out, deal after deal, without ever showing up as a single line item anyone can point to.

Across a typical portfolio, IT and security don’t scale out the way the investment thesis assumes. Each portfolio company runs its own stack, its own vendors, its own security posture, and often its own version of a problem the last portco has already solved; just solved differently, at full cost, with no institutional memory carried forward. The result isn’t a single visible failure. It’s a slow, distributed tax on margin, integration speed, and risk that compounds quietly across the hold period and is genuinely difficult to see from the top, because no one portfolio company’s numbers look wrong in isolation.

This isn’t a technology problem in a narrow sense. It’s a structural one, a byproduct of how portfolios get built. Companies are acquired one at a time, each with its own legacy stack and its own vendor relationships, and “integration” usually means folding financials and reporting lines into the parent, not re-architecting how IT and security operate. Technology gets inherited, not designed. That’s the root of almost everything below.

The Pattern Operating Partners Keep Running Into

A few things tend to recur across portfolios, almost regardless of sector or geography:

• Fragmented tooling and duplicated spending. Portfolio companies independently license overlapping infrastructure, security, and monitoring tools, often the same category of tool, from different vendors, negotiated separately, at separately weaker pricing. None of it was ever designed to consolidate later, so even when someone notices the overlap, unwinding it is its own project.
• Inconsistent security posture. One portco may be mature on cyber governance, with a real SOC and tested incident response; another is running on legacy controls and hasn’t rotated a credential policy in years. From a GP’s chair, there’s no single, comparable view of where the actual exposure sits, which means the portfolio’s real risk is whatever the weakest portco happens to be, and nobody at the center necessarily knows which one that is.
• Slow, expensive M&A integration. Add-on acquisitions, carve-outs, and divestitures routinely stall on IT, systems that don’t talk to each other; duplicate licenses, identity and access sprawl, and onboarding timelines measured in quarters instead of weeks. Every quarter of delay is a quarter of the synergy case in the investment memo that doesn’t materialize.
• No portfolio-wide visibility. IT performance, risk, and asset data usually live in as many formats and definitions as there are portfolio companies. “Uptime” means something different at each one; “critical vulnerability” gets triaged on a different clock at each one. That makes it very hard for an operating partner to know, at a glance, where to intervene, decisions end up being reactive, triggered by an incident rather than a dashboard.
• The build-vs-buy trap. Standing up with an in-house shared services team is slow, expensive, and hard to staff well in a tight technology talent market, particularly for mid-market portfolio companies that can’t offer the compensation or career path a standalone tech function would need to attract strong people. Going without one means every portco solves the same problems independently, at full cost, on its own timeline, every time.
• Innovation stays locked at the portco level. Even where one portfolio company builds real capability in AI, automation, or advanced analytics, there’s usually no mechanism for that capability to travel to the next one. Every portco reinvents the same pilot instead of inheriting what already worked elsewhere in the portfolio.

None of this is any single portfolio company’s fault; it’s what happens by default when technology is never designed to operate across a portfolio in the first place. It’s inherited fragmentation, not mismanagement, which is exactly why fixing it portco-by-portco doesn’t work: each fix is locally rational and portfolio-wide wasteful at the same time.

Why This Stays Invisible Until It’s Expensive

The cost of fragmented technology rarely shows a clean number, which is exactly why it survives quarterly reviews. It shows up as a slightly longer close on an add-on acquisition. A slightly higher renewal quote because nobody negotiated on a portfolio scale. A security incident at one portco that forces an urgent, unplanned audit across all the others because nobody could say with confidence; they weren’t exposed the same way. A talented portco CIO who leaves because they were effectively building the same shared-services function alone that a peer portco built alone eighteen months earlier. Individually, each of these reads as a one-off. Across a five-to-seven-year hold and eight or ten portfolio companies, they add up to a real, if largely uncounted, drag on the return.

What Changes with a Unified Operating Model

The shift that matters isn’t a new tool; it’s treating technology as a portfolio-level operating layer rather than a portfolio-company-level cost center. In practice, that looks like:

• A consistent operating framework across Infrastructure, Cloud, Cybersecurity, Application Engineering, AI, Automation, and Managed Services, so every portfolio company is working from the same playbook, not reinventing it, and a lesson learned at one portco is available to the next one by default rather than by accident.
• A virtual captive delivery model dedicated offshore capability that behaves like an internal team, with continuity and institutional knowledge, without the time, cost, hiring risk, and attrition exposure of building that team from scratch inside every portco.
• Standardized SLAs and governance, which quietly do a lot of the work of reducing operational risk, because service quality stops depending on which portfolio company happens to have the stronger internal team, and “how we handle a P1 incident” stops being a portco-by-portco negotiation.
• Executive dashboards that give operating partners real portfolio-wide visibility into IT performance, security posture, assets, risks, and KPIs, on a common definition, the kind of view that turns technology from a black box reviewed once a quarter into something that can actually be managed continuously, like the rest of the operating model.
• A commercial structure that improves as the portfolio grows, with volume-based pricing benefits as additional companies onboard, so scale, which is normally a source of overhead in fragmented models, becomes a source of leverage instead.
• Built-in support for M&A activity, rapid onboarding of acquisitions, carve-outs, divestitures, and transition services, so integration timelines shrink toward weeks instead of quarters, and the synergy case in the investment memo has a real operating mechanism behind it instead of an assumption.

Importantly, this doesn’t mean forcing every portfolio company into an identical setup or asking a portco CEO to give up control of their P&L to a central IT function. Each business keeps its own contract and its own operating flexibility; the standardization sits at the framework and governance level, not at the level of taking away autonomy. What changes is that decisions about tooling, security posture, and vendor selection stop being made from a blank page at every portco, and start being made from a shared, tested baseline that only gets better as more of the portfolio contributes to it.

Few observations made, “In every portfolio we’ve worked with, the biggest value-creation opportunity in technology isn’t a single big fix, it’s the accumulated cost of each company solving the same problem separately. Once IT and security operate as one framework across the portfolio, GPs stop reacting to individual portco issues and start managing risk and performance at the level they think about the business, the whole portfolio. The firms that get this right treat their technology partner the same way they’d treat a shared finance or HR platform: as infrastructure the whole portfolio compounds on, not a vendor each portco negotiates alone”.

How Do You Know If Your Portfolio Technology is Effective

For operating partners to evaluate whether their portfolio’s technology approach is working, a few honest questions tend to surface the gap quickly:

• Could you produce a single, apples-to-apples view of security posture across every portfolio company today, in the same format, without asking each portco to compile it separately?
• When the last add-on was acquired, how many weeks did it take before its systems and identity access were fully integrated, and was that timeline planned, or discovered?
• If a capability, an AI use case, an automation, a monitoring improvement, proves itself at one portco, is there an actual mechanism for it to reach the others, or does the next portco rebuild it independently?
• Is your portfolio technology spent getting cheaper per unit as it grows, the way procurement and insurance often do at scale, or is each portco still negotiating alone?

The Bigger Picture

For operating partners, technology has historically been treated as something to fix at the portfolio-company level, deal by deal, incident by incident. But EBITDA improvement, faster integration, and reduced operational risk compound differently when they’re engineered at the portfolio level from the start, access to innovation, AI, and automation capability becomes something every portfolio company shares in, rather than something each one has to build alone, on its own budget, on its own schedule.

The firms getting the most out of their portfolios aren’t necessarily spending more on technology, they’re spending it once, at the portfolio level, in a way every portfolio company benefits from, instead of many times over, independently, at the portfolio-company level. That difference doesn’t show up as a single headline number either. It shows up gradually, in shorter integration timelines, fewer surprise security findings, and a portfolio that gets easier to operate as it grows instead of harder.

The post One Technology Partner. One Operating Model. Portfolio-Wide Value Creation. first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

Retailers have never had more visibility into what is happening inside their stores. However, turning that visibility into action is a challenge for retailers. One major U.S. retail chain has implemented digital twins of its stores’ shelving, refrigeration, HVAC and across over 1,700 locations. The retail digital twin market is estimated to be around $5 billion today and is projected to reach $15 billion by the early 2030s. Also, the same retailer has also paired RFID with AR to enable associates to locate any product in seconds rather than minutes.

Another opportunity is to create stores that can observe what is happening, understand what it means and take action before any issues become severe.

The Tuesday Morning Scenario: From Reactive to Adaptive

To see why this matters, consider two different versions of the same Tuesday morning at a grocery store of medium level.

The Old Way (The Reactive Model):

A freezer compressor starts failing overnight. Nobody notices until a morning associate spots warm milk on the shelf. By the time maintenance is called, half the dairy case is a write-off.

Meanwhile, three aisles over, a shopper can’t find the oats milk that’s sitting in the backroom, so they leave and buy it somewhere else. The checkout queue has been eight-deep for twenty minutes, and the one manager who could pull someone onto a register is buried in a stockroom, unaware.

The store has the data, systems, and people in place, but the signals that matter aren’t reaching the right person when action is needed.

The Observe-to-Act Gap

For two decades, retailers wired up their POS systems, cameras, sensors and inventory management platforms. More data was always the assumption. However, a dashboard that displays an issue is not the same as a system that fixes one.

When signals aren’t connected to timely decisions and clear ownership, the consequences go beyond operational inefficiency. The losses that retailers experience include inventory, revenue, employee productivity and customer trust – even when the data could have been preventing those losses is already available.

The result is a growing gap between what the store knows and what it can act upon. The more disconnected signals a store generates without a corresponding action, the harder it becomes to turn technology investments into measurable business value.

The New Way (The Adaptive Model):

The freezer’s digital twin detects the temperature drift six hours prior the compressor fails, opens a work order to replace the unit and reroutes stock before it spoils. The associate’s handheld device pings the exact shelf location of the oats milk in the backroom prior the shopper gives up and walks out of the store.

Nothing here required a fully autonomous store. It required a system that could observe, decide, and nudge a human fast enough for the nudge to matter.

Proof, not a Pitch

The retailer above isn’t a hypothetical. Its digital twin catches refrigeration units drifting toward failure and opens a work order before the freezer goes down not after. Its RFID-plus-AR combination lets an associate scan a product code and get pointed straight to the item, on the floor or in the back.

One caveat worth sitting with: this is one company’s account of its own results; real, in production, at scale, but not independent proof that any retailer gets the same payoff on day one. Store layout, tagging economics, network reliability, and how willingly frontline teams use the tools all decide the outcome. The objective, therefore, isn’t to replicate someone else’s technology stack but to create an observe-to-act loop around the decisions that matter most in your own stores.

The Five-Stage Loop Every Adaptive System Actually Runs

1. Observe- Pull the signals that matters; transactions, orders, RFID events, equipment telemetry, queue length, workforce capacity.
2. Understand – One signal rarely tells the whole story. A thin shelf could mean fast sales, slow replenishment, or a bad record — combine sources before acting.
3. Prioritize- Rank by customer, safety and urgency. A hundred flat alerts are just noise with a UI.
4. Act – Hand it to a clear owner with a due time or trigger a low-risk automation the business has already approved.
5. Learn – Track what happened. Skip this, and false alerts pile up until nobody trusts the system and the whole thing get quietly shelved.

How to Put the Adaptive Store into Practice

Don’t try to boil the ocean. Start with the one store challenge where the problem is frequent, measurable, and already supported by reliable data.

Start with the Right Battleground

Focus on one of five areas:

1. Inventory availability — Catch shelf gaps before they become lost sales.
2. Store fulfilment — Flag at-risk pickup and delivery orders before the customer notices.
3. Asset health — Catch equipment drift before it becomes a shutdown.
4. Workforce coordination — Stop two systems from sending two people to fix the same thing.
5. Customer flow — Use privacy-conscious queue signals to move staff before the line builds.

Then Pilot It the Right Way

1. Start narrow – One event, one expected response, one accountable owner, one deadline, and a clear definition of “resolved.”
2. Run in recommendation mode – The system suggests, a person decides. This is where you catch the false alerts and missing context before the system ever gets real authority.
3. Expand only after proof – On-shelf availability, refrigeration monitoring, and queue response are all reasonable starting points. Widen the system’s authority only once accuracy holds up in your own stores, not someone else’s case study.

In Conclusion

The adaptive store isn’t about having fewer people on the floor. It is about giving the people already there the right information, at the right time, to make better decisions and respond faster.

The sensors, the digital twins, RFID and AR overlays are all in service of one goal: turning real-time signals into timely, meaningful action.

When that loop works, retailers can reduce preventable write-offs, resolve issues faster, improve product availability, deploy their workforce more effectively, and create a smoother customer experience.

The future of retail won’t be defined by how much a store can sense, but by how effectively it can respond. The retailers that close the gap between observation and action will be better positioned to build stores that are not just connected, but truly adaptive.

The post The Adaptive Store: From Reactive to Responsive Retail first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.