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.

As AI gets more affordable, it makes valuing it more challenging.

Running AI is now much more cost-effective. More workloads are economically viable as the leading model providers continue to lower their inference costs and increase their available choices of models. However, cheaper does not necessarily equate to less spent on enterprise AI. The more we use models, have more context, utilize tools, compare, try things out and retest, and more independently work.

This reflects the trend of the cloud era when the reduced infrastructure costs allowed adoption to begin on a wide scale but ultimately led to a new cost-management issue. AI is on the same trajectory. For the technology leaders, it’s no longer about the cost of an AI interaction, it’s about what business outcome the AI workflow produces and what did it cost to deliver?

The Economics of AI is Changing.

The competition from top providers is driving greater options in terms of price and performance, and Chinese and open-weight options are providing alternatives to those who have price considerations for their workloads. Concurrently, the cost of inferences has been decreasing, making more complex AI workflows feasible.

The effect is a new phenomenon that decreases unit costs, accelerating consumption more than the reduction in costs. A token with the lowest price doesn’t necessarily equal the lowest cost to achieve an outcome. Thus, businesses must relate to the actual objectives of the enterprise when using AI investments.

Tokens are still a valuable engineering telemetry and trouble-shooting unit of measure, but not the most useful for executive planning. A CIO is not, ultimately, funding tokens, but he is funding capabilities, products, releases and outcomes.

Such a cost model that is aligned with the delivery makes it possible. Cost per feature reflects investments in design, development, test, documentation and review. Cost per story helps when making plans to deliver the stories, and cost per PR can indicate if AI is driving the delivery or generating rework.

The shift is then from what amount of AI was used to what was produced from the usage and whether or not the results were worth the cost.

Business Outcomes from Tokens

Eventually, the economics of consuming tokens only account for some of the economics of AI, as it goes beyond simple interaction to multi-step workflows. There can be several calls to models, retrieval, execution of tools, evaluation, retries, orchestration, etc., and human involvement in a single task.

There is a change in the economic unit:

Tokens → Interactions → Workflows → Business Outcomes

This is where the concept of AI unit economics comes in handy. How does a leader want to see the total cost of completing a workflow, and not just the cost of the underlying model? This is what is needed. Then they can pose the question: What did it deliver? In what other ways is extra cost being added? Is there a corresponding increase in value for the increased use of AI?

The goal is NOT to reduce the use of AI. It is aimed at maximizing the cost-effectiveness ratio.

Intelligence Orchestration: Where Enterprise Advantage Emerges

The debate over which model is best is no longer the only one that matters as AI is increasingly integrated into more complex processes. It’s the distribution of intelligence throughout the workflow to achieve the desired result.

There can be more than one AI capability used in a single task, ranging from reasoning and retrieval to tool execution and evaluation. If every step is taken in the same manner, then the model may be spending unnecessary funds and at the same time, optimizing only for the cost can result in poor quality. The challenge is to maintain the balance between capability, cost and task requirements.

Orchestration is thus a key component of enterprise AI economics. Rather than making a technology choice on whether to use models, enterprises can consider the management of intelligence as part of the overall process, aligning the capability with the business need and calculating the value of that capability as a function of all costs of delivering it.

Happiest Minds Perspective

On the move from AI Cost Management to AI Value Management.

With the models getting better and cheaper, there is an increasing range of options for enterprises, including frontier, mid-tier models and smaller options as well as options that are open weight. As time has passed, the differentiator is the ability to orchestrate these models around the business task and not access.

Premium intelligence and reasoning may be warranted when a complex architecture decision is in question, and mid-tier intelligence and analysis could be more appropriate for testing, structured code review, or analysis. Smaller, lower cost models can typically perform documentation, summarization, and classification. The open-weight or other models may also be selected for workloads where there are significant economies of scale, control, security, and deployment requirements are met.

The principle is straightforward: use the minimum level of intelligence required to achieve the desired outcome and increase it when the task demands more.

Task complexity, business criticality, data sensitivity, quality requirements, latency and total workflow economics are all considered in effective orchestration. This enables companies to leverage AI as a portfolio and not stick to a particular model or cost structure.

This translates to the transformation from AI consumption to AI value. The winning enterprise will not necessarily use the cheapest model or the most powerful one. It will use the right intelligence, at the right cost, for the right outcome.

The Way Forward

AI economy is moving into a new phase. With the rise of affordable intelligence, consumption will grow and the individual model-call economy will take a back seat to the processes that it facilitates.

Companies that tie AI usage to business outcomes, are aware of the total workflow costs, and can orchestrate intelligence as the business requires will be more successful in scaling their investments in AI.

The aim isn’t just to reduce costs of AI. It’s to maximize the value of each unit of AI use.

The post When AI Becomes More Affordable, Measuring Value Gets Harder first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

From assisting to taking action, AI is becoming a force. For the past several years, enterprises have been leveraging AI to code faster, automate tasks, analyze information, and assist their employees to work faster. It’s the next phase that’s different. By delegating tasks, engaging with tools, connecting with enterprise systems, and handling more tasks without significant human involvement, AI agents are increasingly able to handle more with less human intervention. This opens a greater opportunity and greater responsibility. What can AI do is not enough; enterprises are now asking how they can enable AI to be more independent and remain visible, controllable and accountable to enable safe scaling of the technology.

The first risk is the risk of suggestions from AI assistants that suggest codes. Any agent which can change that code or access enterprise systems or start downstream processes creates another. With AI being so much a part of the job, there must be delineation when it comes to what AI agents can access, modify, and do.

This is further supported by the regulatory landscape. The EU AI Act establishes clear obligations for high-risk AI systems that need to be adhered to, and governance structures and guidelines will take shape in markets, influencing expectations of risk management, security, human oversight, and accountability. Governance should not be considered a compliance task. When done appropriately, it inspires the confidence to empower AI with greater tasks to carry out.

It’s all about confidence and one of the things that helps build confidence is visibility. When agents operate in enterprise workflows, executives should be able to know what’s going on, what resources and information were accessed, if it remained on target or in scope, and its costs. Observability is thus a key aspect of the agentic enterprise. Linking agent actions, permissions, workflows, costs, and outcomes enables organizations to determine if there is any behavior that is happening that they did not expect, scope drift, and if any, to intervene in the process.

Making Autonomy Manageable

This becomes even more complicated when flows of agents is implemented instead of only individual agents. Agents can help with the delivery of software, whether it’s in terms of requirements, development, software testing, software security, or software release. Multiple Agents may run concurrently, interact with shared infrastructure, share resources and make changes within the similar delivery process.

This can’t be achieved with strong models, alone. Enterprises require an operating model that enables agents to be granted permissions, sets up human checkpoints for critical actions, offers visibility into action, and assigns accountability for results. The other agents that run concurrently add other scheduling, resources, conflict and access considerations.

Hence, agentic delivery seems to shift away from being a developer tool and more towards being an operating discipline. The purpose is meant to not restrict autonomy, rather, to keep autonomy manageable as it grows.

It is here that Governance should get closer to execution. Typical controls are used to detect issues after they have happened. A better strategy is to set up particular levels of safety so that when human judgment is needed, you can engage it on that basis rather than taking the chance of it coming up with the wrong conclusion when clearly unsafe activity is present.

Agents ought to be endowed merely with the abilities needed to do their jobs. Any activities outside of “safe” limits should be denied or escalated. Human approval may be needed for high impact activities, like deployments, destructive changes, movement of sensitive data, and cross-system changes. The activity of agents should also be documented to the task, permissions, approvals, and results.

The upshot is a more scalable model; predictable risks are covered by automated controls and decisions that require human judgment are left to people. Governance is not about protection; it’s about enabling greater autonomy of AI.

Happiest Minds Perspective

This shift from AI assistance to governed autonomy is reflected in Rel(AI)Build, Happiest Minds’ approach to agentic software delivery. It brings AI agents across the software development lifecycle while incorporating enterprise controls around access, oversight, traceability, secure execution, and visibility.

Agents run within a scope of task and permission and are subject to specific critical activities such as human approval. Actions can be tracked down to tasks, permissions, approvals and outcomes, and access to sensitive and important information is strictly controlled.

It’s not just about making it easier to automate more software delivery. To provide a setting where organizations can boost AI autonomy without losing sight and control to work with confidence.

Initial pilots suggest that Greenfields can be delivered more quickly, and that measurable quality improvements are achieved, depending on the project, baseline level of maturity and governance readiness. The overall theme of this lesson is that being agentive is more than having a capable model; it has an operating base.

The Way Forward

The next level of Enterprise AI will not only be enabled by the intelligence of the models, but also their confidence in being able to use them.

This confidence is understanding the context in which agents can operate, what they can access, when people need to intervene, and how. When AI is more than just aid, accountability will be the basis for autonomy. By designing this building block early, the organization will be more likely to be able to expand its use of AI and still be able to have the control and visibility to scale.

This is not the future of less surveillance of AI. It’s AI that’s capable of much more, since the enterprise has trusted it to take action.

The post Building Confidence for the Agentic Enterprise first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

The most consequential shift AI drives to software engineering is not that it writes code but that it now performs engineering work. As autonomous agents take on complete tasks rather than individual lines of code, the question for technology leaders is no longer whether AI can code. It is how the software development lifecycle must be redesigned around it. Three shifts will decide who benefits: engineering becoming intent-driven, the rise of governed software delivery, and the economics that determine whether autonomy pays.

From Copilot to AI Teammates

The progression is familiar. Autocomplete predicted the next few lines. Conversational assistants answered questions and generated functions. Agents go further: they plan, use tools, navigate repositories, run tests, and revise their own work until a task is complete. What has genuinely expanded is the unit of delegation, the amount of work a developer can hand over in a single instruction, which has grown from a keystroke to a full ticket.

Because “agent” now describes everything from an inline suggestion to a self-directed system, it helps to grade autonomy the way the automotive industry graded self-driving. Six levels of Engineering Autonomy are mentioned in Figure 1:

• L0 manual work;
• L1 assisted authoring;
• L2 delegated tasks reviewed change by change;
• L3 supervised autonomy reviewed at the pull request;
• L4 bounded autonomy operated within a policy envelope; and
• L5 governed autonomy multi-agent systems own workflows under provable governance.
  
  Figure 1. The Levels of Engineering Autonomy. The unit of delegation, and the governance required to trust it, rise together.

The goal is not to reach L5 everywhere. The appropriate level depends on the risk of the work. A prototype can run at high autonomy; a change to a payment ledger cannot, until the controls exist to trust it. That principle becomes central to everything that follows.

Engineering Is Becoming Intent-Driven

“Intent-driven” is repeated often and defined rarely, and the imprecision matters, because vague intent produces vague software.

It helps to separate three ideas that are routinely confused. A prompt is a passing instruction. A requirement is a business objective. Engineering intent is neither: it is a precise, verifiable statement of what must be true, why, and under what constraints, deliberately separated from how it is built (Figure 2).

The difference from traditional practice is not one of style; it changes where truth resides. In the conventional model, a requirement enters as a ticket, but the working specification lives in a senior engineer’s head and is encoded directly into code. Specification and implementation are fused, and once the system ships, the reasoning behind it is lost. In the intent-driven model, that reasoning is made explicit and durable: acceptance criteria, architectural and security constraints, and the context an implementer needs. The specification becomes the primary artifact, and code becomes a regenerable output derived from it.

Figure 2. Requirement versus engineering intent. Intent moves the locus of truth from code to a durable, verifiable specification.

This reshapes the work rather than reducing it. When implementation can be regenerated, the durable value moves to the intent, and the decisive skill becomes decomposition: breaking an ambiguous feature into units precise enough for an agent to build and a reviewer to confirm. It is worth being honest that this is often harder than writing the code was. The rigor has not disappeared; it has moved up the stack, and the accountability with it.

From Generation to Governed Software Engineering

This is the shift the industry discusses least and will be judged on most.

Two years ago, the question was whether AI could produce useful code. It can. The constraint now is trust. Can we verify the change, explain why it was made, reproduce it later, establish which model produced it, demonstrate compliance in an audit, and be confident that an AI-selected dependency introduces no supply-chain risk? Generation is becoming inexpensive while verification is becoming the dominant cost, and that single movement is why governance is emerging as a strategic capability rather than an administrative one.

The evidence supports this. In METR’s 2025 randomized trial, experienced developers working on their own repositories expected AI to make them roughly a quarter faster and believed afterward that it had; measured objectively, they were about a fifth slower with the tooling available at the time. The explanation is not that AI fails, but that the effort saved in writing is spent again in understanding, validating, and integrating what was written. DORA’s research frames the organizational version: AI is an amplifier. Mature practices, clean architecture, and well-documented systems gain the most, while weak ones have their problems exposed faster. On benchmarks, as agents began resolving a large share of real issues, even established suites such as SWE-bench Verified proved inadequate as frontier measures, prompting harder, enterprise-scale successors. The consistent lesson is that AI does not mend weak engineering; it makes weak engineering visible.

The consequence is that effort moves downstream, into verification in Figure 3. A generated change may be correct, yet teams still need assurance that it meets requirements, respects architecture, satisfies security policy, preserves compatibility, and adds no undue technical debt. Governance is the layer that makes this auditable rather than merely executable, the ability to answer which model produced a change, from which prompt, using which context and tools, against which policies, validated by which tests, approved by whom, and whether the decision can be reproduced months later. In regulated sectors these are already expectations, not aspirations.

Figure 3. As generation becomes cheap, engineering cost migrates downstream into verification and provenance.

To make governance actionable, an assessment is done as a maturity model across six dimensions, each progressing from ad hoc use to assured, compliance-by-construction operation.

Table 1. The Governed SDLC Maturity Model.

The two models combine into a single governing principle: an organization can safely operate at a given level of autonomy only if its governance maturity supports it (Figure 4). Advancing autonomy without first advancing governance is the most common route to fast delivery that no one can stand behind.

Figure 4. The autonomy–governance frontier. Moving right without moving up is the path into the danger zone.

The Economics of Autonomy

Every enterprise program eventually reaches the finance function, and this is where many AI business cases rest on the wrong cost model.

The assistant era was priced per seat. A flat monthly license per developer made return on investment a simple comparison of productivity against cost. Agents break that model because they run on consumption. Each action is metered in tokens, and an agent resolving a ticket does not send one concise request; it loads context, calls tools, reads results, encounters errors, retries, and in multi-agent designs multiplies that work across models. A routine interaction may cost a few thousand tokens; an autonomous task can cost far more. Consumption therefore rises with the level of autonomy an organization is trying to reach, while the per-seat budget it planned around stays flat (Figure 5). The distance between the two is where cost overruns begin

Figure 5. The tokenomics of autonomy. Per-seat pricing is flat; real consumption climbs steeply with autonomy.

Two further costs rarely appear in the business case. The first is the verification effort already described: the labor saved in generation is largely spent again in review. The second is the overhead of governance and the knowledge infrastructure agents depend on. The honest unit of measurement is not cost per line or per generated pull request, but cost per verified feature, and the honest denominator is the total cost of autonomy compute, verification, governance, knowledge infrastructure, and rework combined. Put simply, engineering value is the product of generation speed, context quality, verification quality, governance, and human judgment, and because these compound, any weak factor limits the whole.

The practical pressure lands on budgeting. Finance teams accustomed to predictable per-seat costs now face variable, uneven consumption that few organizations can yet attribute to a team, feature, or outcome. Cost management for AI workloads remains immature almost everywhere, and the first substantial bill from a fleet of autonomous agents is rarely welcome.

The constructive point is that the levers that maneuver cost are the same ones that improve quality. Caching, retrieval in place of loading entire repositories, routing routine steps to smaller models, and limiting agent iterations all reduce token use directly. The knowledge infrastructure that makes agents accurate semantic indexes, architecture graphs, clean ownership and contract metadata also lets them succeed on less context, which is less spend. Context engineering is at once the primary quality strategy and the primary cost strategy. Return on investment in an agentic organization depends far less on model speed than on verification discipline and the quality of context.

The Next Two Years

The direction of travel is becoming clear. Agents will operate as persistent teammates assigned to services rather than tools launched on demand. Specifications will become the primary artifact engineer’s curate. Engineering knowledge decision records, knowledge graphs, contracts, and ownership will carry as much weight as source code. Investment will shift from tools that generate software to platforms that verify, govern, and secure it. And the measures leaders watch will move beyond story points toward autonomous task completion, review effort, verification latency, defect density in AI-generated code, cost per verified feature, and provenance coverage.

The Engineer’s Role Is Rising, Not Shrinking

Every major advance in this field has removed lower-level work and raised the value of judgment, from high-level languages to DevOps to containers to serverless platforms. AI continues that pattern. The defining capability of the coming decade will not be to write code quickly; it will be designing systems in which people and agents collaborate safely, transparently and accountably.

Organizations that optimize only for generation will produce more software. Those that optimize for governed autonomy are capable agents, supported by strong practices, genuine verification, sound economics and accountable governance will offer software that is both faster to deliver and more reliable, secure and trustworthy. Beyond Copilot, the measure of engineering will not be how quickly we write code but how well we direct intelligence, both human and artificial.

The post Beyond Copilot: How AI Agents Are Reshaping the Software Development Lifecycle first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

AI is significantly transforming the software engineering landscape. AI Code Assistants can write code, generate documentation, develop test cases, and speed up daily coding processes. As these features become increasingly commonplace within enterprises, AI is increasingly becoming a part of the software development process.

However, many organizations are finding that “faster coding” does not necessarily equate to “faster software delivery.” The problems engineering leaders still grapple with remain at the forefront: integrating AI in a fragmented way, varying AI development practices, security and compliance worries and lack of visibility of AI-generated work, while individual developers are becoming more productive.

The next big wave of AI adoption isn’t about the proliferation of AI tools. It’s all about embedding AI into a governed software delivery model where it reports measurable results throughout the Software Development Lifecycle (SDLC).

Looking Beyond Individual Productivity

AI coding assistants have shown the swiftness with which repetitive development tasks can be automated. But, enterprise software delivery is more than just coding. A successful release relies on a series of activities that must be connected together, starting from the understanding of the business requirements, the design of the solution architectures, development, test, security validation, compliance and production deployment.

This can lead to organizations making isolated enhancements to their tasks once the AI has been integrated but not affecting the rest of their delivery process. Each team implements its own tools, governance varies, and it is hard to measure the overall impact of AI on engineering performance.

AI should be able to provide value across the entire delivery lifecycle and fit into existing engineering processes and governance.

The Governed Approach to Software Delivery with AI.

This is how Rel(AI)Build works, Happiest Minds’ enterprise-grade AI software delivery framework.

Rel(AI)Build connects subject matter experts from the entire SDLC life cycle, from requirements to architecture design, code, test, security, deployment to production, etc. The key to it is a Hub Orchestrator who understands the business needs, develops plans and coordinates the right AI agents for the execution of the specific stages of delivery.

It involves support for major AI models such as GPT, Claude, Gemini, Llama and is compatible with popular developer tools like VS Code, Cursor, Codex and more. This makes it possible for companies to include AI into their present engineering processes without changing traditional development methods.

Secondly, governance is integrated across the delivery process. The role-based access control, approval workflow, immutable audit trail and built-in security and compliance checks at crucial checkpoints enabling organizations to speed up software delivery while keeping security, compliance and accountability.

Delivering Measurable Engineering Outcomes

The impact of AI is most apparent when it enhances results within engineering teams and not individual development initiatives.

Early pilots and implementations of Rel(AI)Build have shown the ability to deliver 30–50% faster development, 2–3× enhanced developer throughput depending on use case, and over 50% decreased support costs on modernization programs.

Such enhancements are backed by hands-on engineering skills. By leveraging AI code generation, repetitive development work is minimized, and onboarding is sped up in different technology stacks, including Java, .AI-powered testing automates unit, integration, and UI tests, aiding in better defect detection and less regression effort. AI is particularly efficient for legacy modernization efforts, as it can quickly and easily map the architecture and offer documentation in days rather than weeks.

All these capabilities work harmoniously to standardize engineering practices, increase software quality and speed up delivery while maintaining governance.

Bringing AI to scale in a confident manner.

With the increasing adoption of AI, organizations are looking for more than just productivity tools. They require a delivery model that brings visibility, governance, and uniformity to engineering teams.

Happiest Minds has been continually improving Rel(AI)Build, adding additional technology stack support, a VS Code extension, AI quality and efficiency metrics for the evaluation of AI solutions, improved API testing support, and greater coverage for legacy modernization.
Engineering teams are working alongside these advancements to keep the adoption of AI scalable, secure and apt to enterprise software delivery plans.

The impact of AI on software development is already restructuring the industry. The next choice is to use it throughout the software delivery lifecycle, where automation, governance and people come together to give faster releases, higher quality software and measurable business outcomes. That’s where lasting value will drive for enterprises that want to go beyond coding with AI.

The Way Forward

The future of AI in software engineering is no longer about writing more code, faster. It’s about incorporating AI throughout the software delivery lifecycle in a connected, governed, and measurable manner. Beyond point-in-time adoption of AI, enterprises will need to ensure that AI is working alongside with their existing engineering, security and governance practices to scale its impact. The organizations who do this right can go beyond AI-assisted coding to AI-enabled delivery and realize quicker release cycles, higher quality, measurable engineering results. When applied effectively, AI can be more than a developer productivity tool; it can be used to deviate, build, test, secure, deploy and continuously improvise software and ensure the control and accountability necessary at enterprise scale.

The post Beyond AI Coding Assistants: Building Enterprise-Ready Software Delivery first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

A vault only protects what’s locked inside it. The moment a credential is checked out, the vault’s job is done and everything that happens with that access afterward is somebody else’s problem.

This in essence, is the fundamental limitation that have been observed across Privileged Access Management (PAM) deployments throughout the GCC. Organizations across Saudi Arabia and the UAE including some of the largest energy and financial institutions in the region have invested heavily in PAM over the past several years. Passwords are vaulted, sessions are recorded, rotation policies are enforced. On paper, privileged access looks managed. In practice, a wave of lateral movement attacks and insider threat incidents via 2025 and into 2026 has exposed a gap that PAM alone was never designed to close: standing privilege.

The 40% Ceiling

Based on experience, advising critical infrastructure and financial services clients across the region, most PAM programs plateau at roughly the same point. Passwords get vaulted. Sessions get logged. But the underlying access itself – the standing permissions that let an account reach sensitive systems at any hour, on any day, whether that access is actively needed – never goes away. The organization has secured how privileged credentials are stored and checked out. It has not addressed how long that privilege exists once granted.

That distinction matters more than it used to. Forrester and CrowdStrike’s regional threat data both point to the same uncomfortable figure: over 70% of breaches across the Middle East now involve a compromised privileged credential. A vaulted password that sits active around the clock is still a standing door into the network – it’s simply a door with better record-keeping attached to it.

Why the Market Is Moving Past the Vault

The investment numbers tell their own story. IDC MEA estimates put the Middle East PAM market at roughly $280–320 million in 2025, growing at an 18–22% CAGR. That’s a market, enterprises are clearly still committing budget to. But the conversation at GITEX and GISEC 2026 has shifted noticeably from “which PAM platform” to “how do we get rid of standing privilege altogether”, a sign that vaulting and session recording are increasingly being treated as table stakes, not the finish line.

Two forces are driving that shift. The first is operational: as cloud adoption, hybrid infrastructure, and third-party vendor access multiply the number of privileged accounts an enterprise must manage, standing access becomes harder to monitor and easier for an attacker to find. The second is regulatory. Saudi Arabia’s National Cybersecurity Authority, through its Essential Cybersecurity Controls (ECC 2.0), now explicitly mandates privileged access controls for operators of Critical National Infrastructure and auditors are increasingly asking not just whether privileged access is vaulted, but whether it is minimized.

That regulatory pressure is pushing security and infrastructure leaders toward a model built around two related principles: Just-in-Time (JIT) access, which grants privileged access only for the specific task and time window it’s needed, and Zero Standing Privileges (ZSP), which removes persistent access altogether so there is nothing sitting active for an attacker to find between legitimate uses.

From Vaulting Passwords to Eliminating Persistent Access

The architectural shift isn’t about replacing PAM but it represents more mature approach that most organizations haven’t adopted yet. Traditional PAM is fundamentally reactive; it manages credentials that is already present and are granted and it relies on logging and monitoring to catch misuse after the fact. JIT orchestration flips that model. Access is provisioned on request, scoped to a specific task, time-bound by design, and automatically revoked the moment the task is complete – which means there’s no always-on credential left standing for an attacker to compromise in the first place.

For energy operators, financial institutions, and other critical infrastructure entities across the GCC, this shift carries real operational weight. It means re-architecting identity and access workflows around dynamic, task-scoped provisioning rather than static credential vaults. It means integrating JIT orchestration with existing PAM investments rather than ripping them out – the vault still has a role to play, but as one layer in a broader privilege-reduction strategy, not the strategy itself. And it means treating standing privilege as a metric to drive toward zero, not a baseline an organization manages around indefinitely.

The Strategy, Not Just the Tool

None of this diminishes what PAM has accomplished. Vaulting, rotation, and session recording remain necessary hygiene, and they’ve meaningfully reduced credential-related risk across the region’s largest enterprises. But hygiene was never meant to be the destination. As GCC regulators sharpen their expectations and attackers continue to demonstrate how effectively standing privilege can be exploited, the organizations that move fastest from reactive PAM to proactive JIT orchestration will be the ones that turn a compliance requirement into a genuine reduction in attack surface.

The post Why the Middle East Is Moving Past Privileged Access Management Toward Just-in-Time Access first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

The Banking, Financial Services and Insurance industry is entering a new era where competitive advantage will increasingly be shaped by computational power, cryptographic resilience and real-time decision intelligence. While advances in AI, predictive analytics, cloud modernization have significantly enhanced fraud detection, risk assessment and customer engagement; many institutions still face a fundamental challenge; turning insights into timely decisions at scale.

Quantum Computing has the potential to help address this challenge by tackling highly complex optimization and simulation problems that traditional computing struggles to solve. Rather than just speeding up existing models, it can explore and evaluate vast number of possibilities at the same time, allowing banks, insurers and capital market firms to make faster, better-informed decisions.

For BFSI organizations, quantum computing is no longer confined to research labs or future based discussions, It is steadily emerging as a strategic capability with the potential to transform areas including the fraud detection, portfolio optimization, treasury operations, underwriting, liquidity management, cybersecurity and regulatory compliance.

What Classical Analytics Delivered to BFSI

Over the last decades, the BFSI institutions have made substantial investments in digital platforms, data leaks, real time payment infrastructure and AI powered analytics. These investments have significantly enhanced visibility into customer behavior, transaction patterns, credit risks and operational performance.

However, classical analytics largely answers the queries of what is happening and what is likely to happen. Human intervention is still necessary to determine the most impactful course of action. Whether reviewing questionable transactions, approving credit decisions, assessing insurance claims or rebalancing investment portfolios, decision-making quite often indulges multiple systems and stakeholders.

The result is that while insights are generated in milliseconds, decisions often take hours or even days.

Where BFSI Experiences the Greatest Computational Challenges

Banks, insurers and investment firms function in highly complicated environment where decisions often depend on processing enormous volumes of data, interconnected risk factors and constantly changing market conditions. Many financial critical calculations involve millions of variable that must be analyzed quickly and accurately to support business outcomes.

Examples:

• Optimizing investment portfolios across thousands of securities and asset classes
• Managing liquidity and treasury operations in real time amid changing market dynamics
• Derivative pricing and market risk simulations.
• Credit risk assessment across large lending portfolios.
• Catastrophe and actuarial modeling in insurance.

Classical systems can process these scenarios, but they often need approximations, important processing time, or simplified assumptions.

How Quantum Computing Creates Value Across BFSI

Banking

Quantum-enabled optimization can drastically allow portfolio construction, asset-liability management, treasury operations and liquidity prediction. Commercial and retail banks can manage quantum algorithms to assess complex lending scenes, streamline capital allocation and grow enterprise-wide risk management.

Capital Markets and Investment Management

Investment firms continuously look for better methods to enhance portfolios, examine risk exposures and bring trading strategies into effect. Quantum computing can increase Monte Carlo simulations, derivative pricing models and scenario analysis, driving traders and portfolio managers to assess significantly larger solution spaces than traditional methods.

Insurance

Insurance carriers can use quantum computing to grow underwriting, actuarial modeling, catastrophe risk simulation and claims optimization. The capability to exercise multiple risk variables simultaneously can bring pricing accuracy while reinforcing risk-adjusted profitability.

Fraud Detection and Financial Crime Prevention

Financial institutions lose billions in a span of a year because of fraud, money laundering and cybercrime. Quantum-enhanced analytics can find hidden relationships among transactions, accounts, devices and counterparties, increasing earlier identification of fraud rings, mule networks and dubious financial activities.

The Quantum Advantage: From Detection to Decisioning

One of the most significant opportunities for BFSI lies in bridging the gap between detecting risk and taking action.

Today, risk signals frequently move through multiple disconnected systems, including fraud management platforms, compliance engines, core banking applications, claims systems, customer communication platforms, and regulatory reporting tools.

Quantum computing can substantially reduce the complexity of evaluating these interconnected scenarios by identifying optimal actions across multiple constraints simultaneously. This capability enables institutions to move closer to intelligent decision orchestration rather than simple risk detection.

The Critical Role of Quantum-Safe Security

The BFSI industry must also prepare for quantum-related cybersecurity risks. Existing encryption standards used across banking systems, payment networks, customer channels, and insurance platforms may become vulnerable as quantum capabilities mature.

Financial institutions therefore need a dual-track strategy:
1. Explore quantum computing use cases that create business value.
2. Prepare for post-quantum cryptography to protect sensitive customer and enterprise data.

Organizations that delay cryptographic modernization may face exposure to ‘harvest now, decrypt later’ threats, where encrypted information captured today could potentially be compromised in the future.

A Practical Roadmap for BFSI Leaders

Successful adoption needs more than technology experimentation. BFSI organizations should pay attention to:

• Recognizing high-value use cases in risk, treasury, fraud and investment operations.
• Modernizing legacy platforms and allowing API-driven integration.
• Constructing quantum readiness across data, infrastructure and governance.
• Forming post-quantum cryptography strategies.
• Executing targeted pilot programs with measurable business outcomes.

Conclusion

Quantum computing showcases one of the most significant technological shifts facing the BFSI sector. While broad-scale adoption will evolve with time, the institutions that start preparing today will be better positioned to strengthen risk management, drive customer outcomes, build capital efficiency, increase cybersecurity and unlock new opportunities of competitive advantages.

For BFSI leaders, the question is no more whether quantum computing will affect the industry but how quickly organizations can build the capabilities required to capitalize on it.

The post Quantum Computing in BFSI: Transforming Banking, Insurance and Capital Markets first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.

Few weeks ago, an article featured CISO from a mid-sized retail bank explaining their cybersecurity posture, threat detection, endpoint protection, SOC maturity, the usual agenda to his Board. By all accounts, it had gone well.

Two weeks later, their consumer banking app was hit by a credential stuffing attack. Thousands of customer accounts were compromised. Not because their SOC missed something. Not because their endpoint protection failed. Because their login page, the very first touchpoint in the customer’s digital journey was still running on authentication infrastructure designed nearly a decade ago.

Having worked in identity and access management for a long time, one thing conversation confirmed: organizations have spent years hardening everything around their perimeter while leaving the front door essentially unchanged. In 2026, that is no longer a sustainable position.

The Problem Is Not Awareness. It Is Priority.

Across industries, consumer identity has quietly become the most targeted attack surface in digital ecosystems. Yet many organizations continue to treat authentication as a supporting function rather than a strategic capability.

Most security and technology leaders understand the fact that consumer identity is important. The conversation one rarely hear where CIAM is treated with the same strategic urgency as, say, SOC for modernization or a cloud migration.

It is not difficult to understand why. Consumer identity is one of those things that work quietly until something goes wrong. If your authentication platform is functional and customers are getting in without too much friction, it rarely surfaces as a burning issue in a quarterly review. It is infrastructure. It quietly hums in the background.

The problem is that attackers are not treating it as background infrastructure. They are treating it as the highest value entry point into your customer relationships, your transaction data, and your brand reputation.

Account takeover fraud crossed USD 17 billion in projected losses in 2025   surpassing ransomware as the top enterprise security concern for the first time. That number did not come from nation state attackers deploying sophisticated zero days. It came, overwhelmingly, from automated bots replaying stolen credentials against login endpoints that were not designed to tell the difference between a legitimate customer and a machine impersonating one.

That is a solvable problem. But only if it is treated as a priority, not as maintenance.

What I See When I Walk into a Client’s Identity Environment

When team does an IDAM assessment for a new client, there is a pattern often encountered enough which is no longer surprising.

The organization has multiple applications where some are built in house, some are acquired, some are migrated to the cloud over the years. Each of them has its own authentication mechanism. Some use the same underlying identity store; many do not. The result is a consumer identity landscape that is fragmented by design and almost impossible to govern consistently.

A customer logging in through the mobile app has a different experience and a different security posture than a customer accessing the same account through the web portal. Consent records collected when the customer first signed up five years ago live in a database that nobody has reviewed since GDPR came into force. Password reset flows still route through e-mail, the single most phishable channel available.

None of these things happened because someone made bad decisions, but they happened because identity systems were built incrementally, over years to serve immediate functional needs. The strategic picture was nobody’s full-time job.

What we now call CIAM (Consumer Identity and Access Management) as a deliberate, unified discipline is essentially the answer to what happens when you treat that incremental approach as a liability rather than a sunk cost and start from a different set of questions.

The Questions That Actually Matter

In my experience, the organizations that make real progress on CIAM are not the ones that start by asking “which platform should we deploy?”  but when they start by asking three different questions.

Here are the questions organizations should ask.

• First, what does our customer experience at every identity touchpoint, and where is that experience costing us? Abandoned registrations, repeated password resets, friction at checkout or login, these are identity problems that carry measurable revenue consequences. A 2026 CIAM conversation that begins with customer experience rather than compliance tends to get executive attention faster, and for good reason.
• Second, where are we actually exposed, and how would we know? Most organizations have some form of fraud detection. Fewer have continuous risk-based authentication that evaluates every login attempt in context device, location, behavior, transaction value and adjusts the security requirement accordingly without adding steps for the genuine customer. The gap between those two things is where account takeover happens.
• Third, can we demonstrate consent and data governance to a regulator today, if asked? With 19 US states now carrying their own privacy legislation, and GDPR enforcement showing no signs of softening, this question has moved from theoretical to operational. The average cost of managing consumer identity compliance on a legacy stack runs to over USD 3 million annually. That figure tends to focus minds remarkably fast.

AI-Driven Threats

The rise of generative AI is adding a new dimension to identity attacks. Fraudsters can now automate phishing campaigns, create convincing social engineering content, and even leverage deepfake technologies during identity verification processes. As attackers become increasingly sophisticated, static authentication models are struggling to keep pace. Identity systems must become adaptive, continuously assessing trust rather than relying on a single point-in-time login.

The Future of Identity Is Passwordless.

Passwordless authentication is still misunderstood in a lot of organizations.

The business case is not complicated. Passwords are the primary attack vector for account takeover. They are also the primary source of consumer friction   the forgotten credentials, the locked accounts, the reset loops that drive abandonment. Eliminating them addresses both problems simultaneously.

The FIDO Alliance’s research from 2025 ties passkey authentication to a 99% reduction in credential related account takeover across measured deployments. That is not a marginal improvement. It is a structural one.

What clients must know: passwordless is not a project you complete. It is a direction that identity architecture needs to be moving in. Not every application and user segment will get there at the same time. But if one’s CIAM roadmap is not oriented toward that destination, one is building a foundation that the threat landscape is actively working to undermine.

Identity Is Where Customer Trust Lives

Participation is enough post-breach conversations to know what happens to a brand when consumer identity fails at scale. The financial loss is significant. The reputational damage takes longer to repair than most leadership teams anticipate. And the customer trust that was built over years of good experiences evaporates faster than anyone expects.

What is perhaps less obvious   because it does not appear in an incident report   is the inverse: what a well-designed identity experience does for a brand over time. Customers who can securely access your services without friction, who trust that their data is handled properly, and who never have cause to question whether their account is secure, are customers who stay. That relationship is built, transaction by transaction, on a foundation that starts at the login page.

Personal experiences show that framing CIAM as a trust architecture is not just an authentication mechanism but is one that mostly changes how organizations approach this investment. It moves the conversation from cost to value that leads to decisions that actually stick.

The post The Most Dangerous Vulnerability in Your Business Might Be the Login Page first appeared on Digital Transformation Blogs - Bigdata, IoT, M2M, Mobility, Cloud.