Mohankumar Dhanapal
Business Development Manager
AI tools have quietly slipped into everyday business life. Teams now ask copilots to pull data from CRMs, generate reports, and even draft proposals. What began as convenience has turned into access. Every time an AI tool connects to a company system, it effectively gets its own seat inside the network.
These aren’t people, but they behave like users. Each chatbot, automation script, or connector has permissions to fetch and process information. Over time, hundreds of these “non-human” identities start to appear across systems. The trouble is, most organisations have no idea how many exist or what data they can reach.
Identity systems were built to handle employees and vendors, not algorithms that log in at machine speed. There’s no onboarding form or offboarding checklist for an AI agent. It just lives inside the environment, quietly growing in reach. That’s how identity sprawl starts. Not from negligence, but from tools evolving faster than governance.
IAM Boundaries Are Being Redrawn
Until recently, identity management followed a simple chain. HR added a new employee, IT set up access, and security handled oversight. AI has disrupted that flow. These agents don’t belong to any department, yet they hold credentials, make requests, and interact with critical data. No one really owns them, but everyone depends on them.
Role-based access control doesn’t capture how these tools behave. A finance analyst using an AI assistant to review invoices might unknowingly give it visibility into payroll files or customer records. The agent pulls data from multiple systems at once and builds its own understanding of the organisation. When that happens through a shared token or API key, the IAM platform can’t tell whether it was the employee or the AI making the call.
That’s where the boundaries blur. Access control is no longer about who logs in, but about what kind of entity is acting on behalf of the business. Until IAM evolves to recognise these digital actors, enterprises will keep granting unseen identities a level of trust that was never intended for them.
Data Permissions in the Age of Prompts
Every prompt to an AI system is, in some way, a data request. When an employee asks a chatbot to summarise a client proposal or extract insights from project files, they are indirectly granting it access to sensitive information. The tool reads, processes, and sometimes stores that content to generate an answer. The convenience is undeniable, but so is the exposure.
The real challenge is visibility. Most organisations don’t track what data an AI tool touches once it’s connected. A chatbot integrated with SharePoint, email, and Teams can move across departments without anyone noticing. The information it handles isn’t always meant to travel that far, but AI models don’t understand context, they only follow instructions. That’s how confidential data ends up in logs, caches, or external servers where no one expected it to go.
Traditional access controls can’t keep up with this pattern. Data doesn’t leak because of a weak password; it leaks because a well-intentioned prompt exposed something the system wasn’t meant to share. The old idea of least privilege doesn’t apply when every query could combine data from different sources.
For CISOs, this changes the conversation around governance. It’s no longer enough to secure users. They have to secure what those users can make AI tools do on their behalf. That means treating every AI interaction as a potential access event, monitoring which systems are being queried, and defining clear boundaries around what information can be used in prompts.
The smarter the tools get, the more critical identity becomes. Managing who can use AI is one layer of control; managing what the AI can see and learn is where true governance starts.
What Identity Governance Must Evolve to Handle
Most identity systems were built for predictable users. They manage logins, apply roles, and revoke access when people leave. AI doesn’t follow that rhythm. It doesn’t join, it doesn’t resign, and it never takes a holiday. Once connected, it keeps operating silently in the background: analysing data, automating tasks, and sharing outputs with whoever prompts it. That means identity governance now needs to think beyond human accounts.
The first step is recognising AI agents as identities in their own right. Every connector, plug-in, or automation should be treated like a digital user with a defined purpose and scope. Just as you wouldn’t give an intern admin privileges, AI shouldn’t have unrestricted access to enterprise data. The principle of least privilege has to extend to machines as well.
Governance teams also need to understand data flow at a deeper level. It’s not just about whether access is granted, but how that access is used once the AI starts working. An AI assistant trained to summarise financial reports shouldn’t have visibility into HR records. Without clear segregation, the system ends up mixing information from different domains and that’s how sensitive data slips through unnoticed.
Another shift involves accountability. In most organisations, AI access is granted informally. A project lead might enable an API key or connect a tool for convenience. Months later, no one remembers who set it up or what data it still touches. Governance needs to close that gap by tracking ownership, access history, and usage patterns for every AI identity.
This isn’t about slowing down innovation but about keeping control as automation scales. The enterprises that get this right will be able to use AI confidently, with the same level of assurance they have for human users. And that’s where identity governance becomes more than compliance. It becomes the foundation for safe and sustainable AI adoption.
Turning the Lesson into an Advantage
AI is changing how businesses operate, but it’s also rewriting the rules of security. The organisations that treat identity as the foundation for AI use gain more than just protection. They gain agility. When you know which AI tools can access which data, and who is responsible for them, you can innovate without constantly worrying about accidental exposure or compliance issues.
CISOs who take this seriously can turn a potential risk into a strategic advantage. Properly governed AI isn’t a liability; it’s a capability. Teams can deploy new tools faster, automate more processes, and provide richer insights to decision-makers, all while maintaining visibility and control. The key is simple: treat AI like a user, monitor it like a user, and enforce governance policies consistently.
It also changes the conversation internally. Security is no longer just about blocking risks. It’s about enabling growth. When executives see that AI tools are controlled, monitored, and compliant, they become more willing to experiment and adopt automation. That leads to smarter decisions, faster delivery, and a culture that embraces innovation responsibly.
Ultimately, identity governance becomes the lens through which the business views AI safely. Those who act now are not just avoiding mistakes. They are positioning themselves as leaders in the AI-driven future. It’s a chance to turn compliance and security into real business value.
