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Phone numbers are simple. Validating phone numbers is surprisingly hard. Many developers assume a phone number is just a fixed-length string of digits. In reality, phone formats are dependent on country, carrier, dialing situation, and user preference. What validates well in one market can easily invalidate good users in another or allow bad data into your system.

This challenge becomes more apparent when applications accommodate international users or multiple input channels. React phone number validation is one such example. Developers need to find the right balance between user-friendly formatting and accurate validation logic. That can be very helpful, as long as you have the right tools and methods in place. But if the front end, back end, and database don’t all follow the same rules, users may run into confusing errors or inconsistent results.

Relying on Simple Regex Patterns

One of the most common mistakes is to use a simple regular expression such as “only 10 digits” or “must start with a plus sign.” Regex can help check simple structure, but it’s rarely enough for true phone validation.

For example, a 10-digit rule could work for many US numbers but will not work for valid numbers from the United Kingdom, Germany, India, Brazil, and many other countries. Even within the same country, mobile, landline, toll-free, and premium-rate numbers may have different patterns.

To avoid this problem, use a reliable phone number parsing library that supports international numbering plans. Libraries built on Google’s libphonenumber are a great option as they consider country-specific rules, formatting, and number types.

Disregarding Country Codes

Another common problem is checking a number when you don’t know the country. Local phone numbers may only be valid for a specific country or region. For instance, the same string of numbers can have different meanings depending on whether the user is in the United States, Australia, or France.

Developers must not require users to provide a phone number without collecting or deducing the country code. A country selector, geolocation hint, or account profile setting can help determine how to interpret the number. However, geolocation should only be used as a convenience, not as a hard-and-fast rule, as users may be traveling or using foreign numbers.

Mistaking Formatting for Validation

Validation and formatting are related, but not the same. Formatting a number makes it easier to read, like showing “(415) 555-2671” instead of “4155552671.” Validation is the process of determining whether the number is structurally possible or actually valid for a region.

A common mistake is to dismiss numbers just because they aren’t displayed the way you want them to be. Spaces, hyphens, parentheses, or international prefixes can be entered by users. Normalize your input and validate it in your application. Remove unnecessary characters, convert international dialing prefixes as needed, and store numbers in a consistent format, e.g., E.164.

Not Normalizing Stored Numbers

Storing phone numbers exactly as users enter them can cause problems down the road. Detection of duplicated content is difficult, searches become unreliable, and integrations with SMS or voice providers may fail.

For example, one user might enter “415-555-2671,” another might enter “+1 415 555 2671,” and another might enter “(415) 555-2671.” These may be the same number, but your system may see them as different values.

The best practice is to store a normalized version, typically in E.164 format, such as “+14155552671. “You can still present the number in a friendly local format, but your internal systems should use the normalized value.

Blocking Valid Cases

Some validating systems are too strict and reject valid numbers. This is common when developers assume all phone numbers are at least or at most a certain length based on local examples they are familiar with. Phone numbers for international locations can be very long.

Other edge cases are extensions, short codes, toll-free numbers, emergency numbers, and numbers that are valid for voice calls but not for SMS. If your application needs SMS delivery specifically, structural validation is not enough. You might want to check whether the number type can receive text messages or go through a verification step.

Omitting Server-Side Validation

Client-side validation can improve user experience but should never be the only layer of validation. Users can bypass front-end checks, APIs can extract data from third-party systems, and mobile apps may lag behind web updates.

Always validate phone numbers on the server before you store or use them. So ideally, the server is the source of truth and the client is providing helpful feedback in real time. This avoids inconsistent rules and protects your application from malformed data.

No Check for Ownership

A number that looks valid is not necessarily the number of the person typing it in. After validation, verification should be done for account creation, password recovery, two-factor authentication, and sensitive notifications.

The usual verification method is using SMS or voice one-time codes. They verify the number is accessible to the user at that time. Developers should also consider rate limiting, fraud prevention, and fallbacks for users who can’t receive messages.

Conclusion

Validating phone numbers is harder than it looks. Phone numbers are global, variable, and context-dependent. Most of the problems can be avoided by using trusted parsing libraries, collecting country information, normalizing stored values, validating on the server, and verifying ownership when necessary. This thoughtful approach can help reduce user frustration, improve data quality, and make communication features more dependable.

The post Common Phone Validation Issues Developers Face and How to Avoid Them appeared first on TechPluto - Latest Startup & Tech News.

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Remote teams depend on file sharing more than almost anything else. Every project boils down to getting the right document to the right person when they need it. And most teams mess this up constantly. People email attachments back and forth, save over each other’s work, leave folders open to anyone with a link, or send sensitive files with little attention to encryption.

The annoying thing is that these problems aren’t difficult to fix. They just go unnoticed until someone misses a deadline or a critical file vanishes. Here’s a look at the four biggest file-sharing mistakes remote teams make, and what you can actually do about them.

Mistake 1: Emailing Attachments Instead of Sharing Links

This has been a problem since remote work took off, and it’s still everywhere. Someone finishes a draft, attaches it to an email, and sends it to three colleagues. Each person downloads their own copy, makes edits on their machine, and emails their version back. By the end of the day, there are four different versions of the same document floating around. Nobody knows which one is current.

Email was never designed to be a complete document collaboration system. Once you attach a file to an email, you’ve created a separate copy that lives outside the team’s shared workspace.

Now multiply that across a team of ten or fifteen people. You’ll end up with dozens of disconnected copies sitting in inboxes and downloads folders.

The fix is simple: stop attaching files whenever collaboration is required and start sharing links from a cloud platform. When everyone works from the same source file, edits happen in one place. There’s no confusion about which version is the latest because the team is working from the same document.

For remote teams looking at the wider role of cloud storage and collaboration tools, TechPluto’s guide to tools for managing remote teams effectively covers cloud storage, document collaboration, communication, and other parts of the remote-work stack.

Mistake 2: Duplicate Versions With No Clear Original

Even teams that use cloud storage fall into this trap all the time. Someone downloads a file to work on it offline, re-uploads it with a slightly different name, and suddenly there are two versions in the same folder. Or someone copies a file into a personal folder “just in case” and forgets about it for weeks.

The real issue is usually a lack of version control.

If your storage platform doesn’t track changes automatically, people will make up their own workarounds. And those workarounds almost always create confusion.

Modern cloud storage can make this much easier. NIST’s security guidelines for storage infrastructure specifically identify features such as change tracking, versioning, access permissions, auditing, and encryption as important capabilities in cloud-based storage environments.

Teams that use online storage with built-in versioning can avoid much of this problem. The platform keeps a history of changes, making it easier to see what was edited, when it happened, and who made the change.

Here’s a quick test: if you’re renaming files with things like “final_v2_REAL_final,” your system isn’t working.

If your platform doesn’t handle versioning automatically, agree on a naming convention upfront. Something like ProjectName_YYYY-MM-DD_v1 will at least make it obvious which file is newest.

Mistake 3: Wrong Permissions on Shared Files

This one goes in two directions, and both cause headaches.

Some teams share everything with full edit access, which means anyone can accidentally delete or overwrite a file. Other teams lock things down so hard that people can’t get to what they need without sending a request and waiting for approval.

Neither extreme works.

Oversharing leads to accidental edits and unnecessary exposure. Locking everything down creates bottlenecks where people sit around waiting for access instead of getting on with their work.

The answer is granular permissions.

Most modern cloud platforms let administrators set different levels of access, such as view-only, comment-only, or full edit permissions, depending on the file or folder.

Spend ten minutes setting these up when you create a shared workspace, and you’ll save yourself hours of frustration later.

It’s also a good idea to review permissions periodically, particularly if team members have changed roles or left the company.

NIST’s guidance on secure information exchanges recommends protecting information according to its risk before, during, and after it is exchanged or accessed. That principle applies well to remote file-sharing workflows.

Mistake 4: Sending Sensitive Files Without Encryption

This carries the most risk and often gets the least attention.

Remote teams share contracts, financial data, client information, and internal strategy documents on a regular basis, sometimes through tools without carefully configured security controls.

NIST’s guidance on secure file exchanges over the internet specifically addresses the risks associated with exchanging files through email attachments, file-sharing services, and other internet-based methods. The guidance emphasizes protecting the confidentiality and integrity of files and file exchanges through appropriate security measures.

Standard cloud storage commonly protects data in transit and at rest, but the exact security model varies between providers and products. End-to-end encryption works differently because the provider is designed not to have access to the keys required to decrypt the content.

For teams handling highly confidential information, that distinction matters.

Before choosing a file-sharing platform, check what type of encryption it actually provides, who controls the encryption keys, and what access administrators and service providers may have.

Better Defaults Will Save You More Than Better Tools

Most of these mistakes don’t come from using bad tools. They come from not setting up good tools properly.

A team can have a capable cloud platform and still end up with a mess if nobody agrees on folder structure, naming conventions, permission levels, version control, or who owns what.

Pick a platform that supports versioning, granular permissions, access controls, and appropriate encryption. Then spend some time setting it up properly before everyone starts uploading files.

For businesses moving more of their work into cloud-based environments, TechPluto’s guide on how to run a small business remotely also covers cloud storage, collaboration tools, communication, and other infrastructure that remote teams commonly rely on.

The goal isn’t to eliminate every possible file-sharing mistake. It’s to create sensible defaults that make the common mistakes harder to make.

Remote work depends on people being able to access and collaborate on the right information quickly. With a clear file structure, sensible permissions, reliable versioning, and appropriate security controls, teams can spend less time hunting for documents and more time actually working.

The post Four File-Sharing Mistakes That Slow Down Remote Teams appeared first on TechPluto - Latest Startup & Tech News.

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AI agents are quickly becoming part of everyday business software. Unlike traditional chatbots, they can do more than generate answers. An AI agent can fetch business data, call APIs, use software tools, coordinate multi-step workflows, and operate with limited human supervision.

That is what makes agents valuable — and what makes them an increasing security concern.

An agent connected to a public knowledge base may have relatively limited exposure. The consequences can be far greater when an agent is connected to a company’s CRM, cloud storage, internal documents, or production systems and its permissions are misused or its instructions are manipulated.

So, in 2026, enterprise AI security is about more than protecting AI models. Organizations also need to know who an AI agent is, what it can access, what tools it can use, and what actions it can take.

How AI Agents Change the Security Equation

For a long time, enterprise software has been built around predefined rules. AI agents are more flexible because they can interpret information, select tools, and determine the next step in a workflow.

That autonomy creates a different security boundary.

NIST has been examining identity and authorization for software and AI agents, including authentication, authorization, least privilege, delegation, auditing, and non-repudiation. The agency’s work is part of a growing effort to establish appropriate controls when agents interact with enterprise resources.

The problem is not that an AI agent is inherently unsafe. The problem is that an autonomous system can potentially turn its permissions into actions.

For enterprises, traditional access control therefore needs to be considered alongside an agent’s ability to interpret instructions and interact with tools.

The Biggest AI Agent Security Risks

Agent Hijacking and Prompt Injection

Prompt injection continues to be a major security concern for agentic AI.

An attacker can place malicious instructions inside content that an agent is expected to process. Depending on the system design, those instructions can influence the agent’s behavior.

This becomes more serious when the agent can use external tools.

For example, an AI agent might be asked to research information from a website. If that website contains instructions designed to manipulate the agent, the system could encounter content that appears to be ordinary information but is actually an attempt to influence its behavior.

NIST has specifically studied indirect prompt injection and agent hijacking, highlighting the need for controls that address both model behavior and the tools available to agents.

Google Threat Intelligence has also documented the move toward more autonomous AI-assisted workflows in cyberattacks, showing how agentic capabilities can affect both offensive and defensive security operations.

Too Many Permissions

The principle of least privilege is especially important for AI agents.

An agent should have only the permissions required for its assigned task. A customer-support agent might need access to customer records and the ability to create support tickets, but it may not need permission to delete accounts or change administrator settings.

The more systems an agent can access, the greater the potential impact of a compromised credential, malicious instruction, or unexpected model behavior.

NIST’s work on AI agent identity and authorization specifically considers authorization, least privilege, delegation, auditing, and related security controls for autonomous software agents.

The principle is straightforward: an agent should have enough authority to complete its job, but not unnecessary authority that expands the consequences of an error or compromise.

Sensitive Data Exposure

AI agents can be useful precisely because they can combine information across several systems.

An enterprise agent might pull information from a CRM, internal documents, analytics platforms, email, or a database during a single workflow. That raises an important question for security teams: What information can the agent access once those systems are connected?

Data access therefore needs to be considered at the workflow level, not only at the individual application level.

This becomes particularly important as businesses move from simple AI assistants toward more autonomous systems capable of coordinating several tools in one process.

AI Agents Require Clear Digital Identities

One of the biggest changes in enterprise security is the emergence of AI agents as another form of non-human identity.

Companies have managed service accounts, applications, APIs, and automated systems for years. AI agents add another dimension because they can perform increasingly complex tasks on behalf of employees.

Security teams should be able to identify their production agents and answer straightforward questions:

• Who owns the agent?
• What does it do?
• Which systems can it access?
• What permissions does it have?
• Which credentials does it use?
• Who granted those permissions?
• What actions has it taken?
• How quickly can its access be revoked?

NIST’s 2026 work on software and AI agent identity directly addresses challenges around authorization, auditing, delegation, and non-repudiation.

The goal is not to treat AI agents like employees. Instead, organizations need a reliable way to understand and control the authority they give to autonomous software.

Businesses building agents can also benefit from understanding how to build AI agents for startups, particularly when security controls need to be considered alongside deployment and scalability.

MCP Is Adding New Tool-Access Considerations

The increasing adoption of the Model Context Protocol (MCP) has brought even more attention to the security of AI agents.

MCP provides a standardized way for AI applications to interact with external tools and data sources. That makes agents more useful because they can interact with systems outside the model itself.

But every connection creates another security boundary.

In 2026, the NSA published security design considerations for AI-driven automation using MCP. The guidance addresses concerns including dynamic tool invocation, trust boundaries, context sharing, and agent misuse.

The official MCP authorization specification also addresses authorization and security considerations around access and token handling.

For enterprises, attaching an agent to another application should therefore be treated as adding another capability — not simply installing another integration.

How Businesses Can Protect AI Agents

Make Sure Each Agent Has a Distinct Identity

Organizations should maintain an inventory of AI agents operating in production and assign clear ownership to each one.

Security teams should be able to distinguish one agent from another in authentication and activity logs.

Use Least-Privilege Access

Grant agents only the permissions they need.

Where practical, separate read and write permissions and restrict access to the specific systems required for the workflow.

This limits the potential impact if an agent is manipulated or its credentials are compromised.

Watch Agent Activity

Logging should go beyond simply knowing whether an agent is running.

Organizations should monitor tool usage, authentication events, data access, permission changes, and unusual sequences of actions.

This visibility becomes particularly important when an agent interacts with several applications during a single task.

Require Approval for High-Risk Actions

Not every agent action requires human approval.

However, sensitive operations such as deleting records, changing access controls, transferring money, or modifying production infrastructure may require additional authorization.

A practical approach is to allow greater autonomy for routine tasks while applying tighter controls to actions with significant consequences.

Test Against Prompt Injection

AI agents should be tested against realistic attack scenarios, including malicious webpages, documents, emails, customer messages, and other external content they may encounter during normal operation.

Security testing should not only determine whether a model follows instructions correctly. It should also test whether an agent can be manipulated into misusing its connected tools.

For businesses looking at the broader shift in AI security, conventional security measures may not be enough for AI agents, particularly when autonomous systems have access to business-critical resources.

AI Agent Security Is Becoming an Enterprise Discipline

The security discussion around AI agents is moving beyond model-level protection.

NIST launched its AI Agent Standards Initiative in 2026, focused on secure and interoperable agent ecosystems. The initiative is part of a broader effort to address the standards and security challenges emerging as agentic AI becomes more capable.

At the same time, threat-intelligence researchers are documenting how increasingly autonomous AI systems are being incorporated into real-world attack workflows. Google Threat Intelligence’s research on the evolution of adversarial AI describes adversaries moving from basic prompting toward more autonomous AI-assisted operations.

For businesses, the message is straightforward: AI agents should not be treated as ordinary chatbots with a few extra features.

They are software systems that can access resources, interpret information, make decisions, and take actions.

That makes identity, authorization, least privilege, monitoring, tool security, and rapid revocation important components of a responsible AI-agent architecture.

The real security question in 2026 is not whether businesses can use AI agents.

It is whether they can give those agents enough authority to be useful while keeping that authority visible, limited, and controllable.

The post AI Agents Are Becoming a Security Issue: What Businesses Need to Know in 2026 appeared first on TechPluto - Latest Startup & Tech News.

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Digital businesses are increasingly facing a problem that is difficult to detect with traditional analytics: not every visitor is a unique, legitimate user.

One person can have multiple accounts, repeatedly take advantage of promotional offers, share a paid account, or use a VPN, proxy, or anti-detect browser to access a service. That activity can distort traffic data and create financial and security risks for SaaS companies, marketplaces, fintech platforms, and other online businesses.

Founded in 2025, SaaS company ShieldLabs is building fraud detection and prevention technology with traffic quality scoring. The platform is designed to detect users running multiple accounts, account sharing, and account takeovers under any masking, helping companies prevent losses from free-trial, bonus, promotional, and API abuse.

What ShieldLabs Can Do

ShieldLabs describes itself as fraud detection and prevention with traffic quality scoring. Its technology is designed to help businesses understand traffic quality while detecting different forms of fraudulent and abusive activity.

The platform analyzes signals across devices, browsers, operating systems, IP addresses, and networks. ShieldLabs says it directly detects multi-accounting, account sharing, account takeover, and impossible travel out of the box, with each detection assigned a Medium or High confidence level.

These capabilities can be particularly relevant for businesses where abuse can quickly translate into financial losses. SaaS companies, for example, may need to identify users repeatedly creating accounts to exploit free trials, while platforms offering promotions or bonuses may need to distinguish legitimate users from coordinated abuse.

The Significance of Anonymous Traffic

For many online businesses, fraud prevention happens after suspicious activity has already taken place. ShieldLabs takes an earlier approach by examining visitors, devices, accounts, and surrounding signals before an organization has to decide how to respond.

Consider a SaaS company offering a free trial. One person could potentially create multiple accounts and repeatedly use the offer. A marketplace could encounter users operating multiple accounts, while a business running paid advertising could find that some of its traffic is coming through anonymizing services.

ShieldLabs focuses on use cases including multi-accounting, account sharing, account takeover, promotional abuse, traffic-quality analysis, and related forms of online abuse. By connecting visitors, devices, and accounts, the platform aims to make relationships between seemingly separate activities easier for businesses to identify.

For companies where user acquisition and conversion metrics directly affect business decisions, identifying suspicious traffic can also provide an analytics benefit. Traffic-quality information can help businesses understand where visitors are coming from and distinguish potentially abusive activity from legitimate traffic.

A Clear Alternative to Black-Box Risk Scores

One of the notable aspects of ShieldLabs’ product is its focus on explainability.

Instead of presenting only a risk score, the platform shows the named signals that contributed to it. These can include VPN, proxy, Tor, Apple Private Relay, datacenter IP, anti-detect browser, and browser automation.

ShieldLabs says its platform also detects bots and AI agents and separates bad bots from good ones.

The product generates a Risk Score from 0 to 100 for every visitor, user, device, and IP address, alongside the named signals associated with that assessment. This gives teams more context when deciding how to handle potentially suspicious activity.

According to ShieldLabs, the platform has 99.9% identification accuracy and 99.9% risk signal detection accuracy.

Built for Smaller Teams

ShieldLabs is also positioning itself as an alternative to the cost and complexity traditionally associated with enterprise fraud and visitor-intelligence products.

The company offers self-serve access and transparent pricing. Its free plan includes 5,000 identifications.

The paid plans include Starter at $99 per month for 25,000 identifications, Growth at $399 per month for 150,000 identifications, and Scale at $999 per month for 500,000 identifications. ShieldLabs also offers discounts of up to 20% on annual billing.

Implementation can start with a JavaScript snippet, after which teams can connect the platform to their systems through APIs and webhooks. ShieldLabs’ documentation provides integration options for different visitor and user scenarios.

Where It Might Be Useful

ShieldLabs serves multiple industries.

The company identifies SaaS, fintech, e-commerce, marketplaces, travel, media and streaming, ticketing, gaming, and other online businesses as potential use cases.

The underlying idea is similar across these markets: determine whether seemingly different accounts, sessions, or visitors may actually be connected and identify signals that indicate elevated risk.

Its technology can help businesses identify activity associated with multi-accounting, account sharing, account takeover, promotional abuse, and other forms of suspicious behavior.

For security teams dealing with credential stuffing, visitor and device-level signals can also provide another layer of context alongside existing authentication and fraud-prevention systems. Organizations such as OWASP have well documented the broader cybersecurity challenge.

The company also highlights Web3 as another potential application, where identifying connections between visitors and accounts can help detect potential Sybil activity.

Detection That Helps Block Fraud

ShieldLabs’ approach is to provide the detection and risk intelligence that businesses can use within their own systems.

The platform identifies users and sessions, analyzes relevant signals, and provides a Risk Score alongside the signals behind it. From there, businesses can allow the visitor through, add a verification step or block the action.

That could mean allowing a visitor to continue normally, challenging the visitor with additional verification, or blocking the activity altogether.

This approach gives development and security teams flexibility. ShieldLabs can act as an intelligence layer that feeds information into an existing authentication, fraud, or moderation system rather than replacing it.

This can be particularly useful for businesses that want to introduce additional risk signals without completely redesigning their existing user-management infrastructure.

The Road Ahead

ShieldLabs is still a young company, founded in 2025. Its product is built around challenges such as anonymized traffic, anti-detect browsers, multi-accounting, account sharing, account takeover, bots, AI agents, and other forms of online abuse.

The longer-term question will be how effectively its detection signals perform across different environments and how businesses translate those signals into practical decisions.

For now, ShieldLabs is taking a focused approach: identify who is visiting, expose the signals behind their activity, and give businesses more context before they decide what to do.

As online businesses face increasingly sophisticated forms of abuse, better visibility into who — or what — is actually behind a visit could become an increasingly important part of maintaining trustworthy traffic, accurate analytics, and sustainable customer acquisition.

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