AI Has Given You Two New Problems – And Identity Governance Is the Only Place They Meet
AI has quietly turned identity governance into the place where real power flows are decided—who (or what) can move money, change code, or rewrite records. This shift has handed CISOs and CIOs two problems nobody really signed up for: AI inside the identity stack making access decisions, and AI acting as powerful identities across the business.
The Incident That Makes This Real
The incident that illustrates this issue is straightforward: an AI “assistant” in ITSM is flipped from “recommend” to “auto‑execute,” quietly begins approving risky firewall rules and configuration changes, and only becomes apparent when the board questions how a helper account gained de facto admin powers. This situation arises from a classic blind spot—an unsponsored AI account with production-level powers and no audit trail for who enabled it, what it can access, or how to shut it down safely.
You don’t just “have AI” now. You have AI in two critical areas:
- AI inside your identity stack, shaping who gets what access.
- AI acting as identities across your business, performing tasks traditionally handled by humans.
Both of these are already in production in most enterprises, while governance remains in pilot mode in many organizations.
The Evolution of Identity Governance
For years, identity governance revolved around policies, workflows, and reviews. This process was slow and often painful, but at least it was clear who made the decisions: managers, application owners, and risk teams. This dynamic is now changing.
Modern IGA platforms increasingly rely on AI to:
- Cluster similar access requests.
- Flag anomalous entitlements.
- Suggest “approve/deny” decisions to alleviate reviewer fatigue.
As a result, algorithms are now significantly influencing access, alongside written policies.
Challenges for CISOs and CIOs
For CISOs, this raises uncomfortable questions regarding trust and explainability. If an AI-assisted recommendation leads to granting a high-risk entitlement, can you explain to an auditor or regulator why that decision was sensible at the time? If the model learned from a flawed baseline—like years of over-privileged access—it can normalize the very behaviors you’ve aimed to eliminate, but at machine speed.
For CIOs, the challenge is different but equally complex. You require IGA that can keep pace with SaaS, cloud, and AI projects without turning every sprint into an access bottleneck. AI appears to be the only feasible way to clear the backlog of low-value approvals and routine reviews. However, without clear parameters, “optimization” may devolve into invisible automation, blurring the lines between human judgment and AI decisions.
The Leadership Test
The leadership test is straightforward: if AI influences identity decisions in your environment today, can you demonstrate where, how, and who oversees those decisions? Can you provide evidence to a board, regulator, or legal counsel if asked? If the answer is no, your identity program is already lagging behind your AI program.
The Second Problem: Non-Human Identities
The second problem is more visible yet harder to manage. Recent CISO AI risk data indicates that non-human identities, including AI agents, now rival or exceed human accounts in many environments. However, few organizations can pinpoint where these agents have access. These agents perform various tasks: opening tickets, routing incidents, merging code, moving data, closing cases, and interacting with systems of record. Every time an AI system alters a state in a production system, it effectively becomes a new operator.
The industry still refers to these systems as “features” or “bots.” In contrast, identity programs are typically centered around humans, resulting in a significant blind spot regarding non-human identities. Most organizations that are mature in managing human identities are nearly oblivious to AI agents, which often operate with shared secrets, tenant-wide tokens, or unchecked API keys; they rarely feature in access reviews and seldom trigger alerts if their permissions expand.
AI Agents: A New Class of Insider
From a CISO’s perspective, these AI agents resemble a new class of insider. They are tireless, they never forget a credential, and they can operate at a scale beyond human capability. When misconfigured or misused, they can become policy-driven breach engines, executing commands exactly as directed but across unanticipated areas.
For CIOs, these agents manifest as architectural debt masquerading as innovation. Every “quick win” AI integration lacking identity patterns contributes to access sprawl and operational opacity. When an outage occurs, it becomes challenging to determine whether the issue stemmed from a human change or an AI action. Platform teams often struggle to identify which underlying service account corresponds to which “assistant” or what will fail if someone disables it.
The Need for Identity Governance
The pivot must involve treating AI systems as identities in their own right. Each AI agent should have an owner, a defined business purpose, and a risk tier; its entitlements must be articulated in policies rather than hidden in application-specific configurations. Moreover, AI agents should appear in reviews, certifications, and incident timelines like any other powerful user. Once AI is recognized as an identity, the natural governance framework should reside within the identity governance control plane.
Convergence of AI and Identity Governance
AI inside IGA and AI as identity may seem separate, but they converge operationally on the same fundamental questions:
- Who owns this AI system?
- What can it access, change, or trigger?
- How do we detect when its behavior or access changes in significant ways?
- What evidence can we provide that it is under control?
Initially, you can address these questions via ad hoc scripts, application-specific consoles, and committees. However, this approach will not scale. The only sustainable solution involves managing both AI types together within your identity governance control plane—where humans, machines, and agents coalesce under a unified identity model, subject to the same lifecycle and policy controls.
A Shared Agenda for CISOs and CIOs
This creates a shared agenda for CISOs and CIOs:
- Build a unified inventory of human and non-human identities, complete with clear risk tiers and accountable owners.
- Establish explicit rules for where AI can recommend actions and where it can execute them, ensuring visibility in runbooks, platforms, and review workflows.
- Integrate AI identity signals—such as new agents, changing scopes, and unusual access patterns—into your detection and resilience programs, extending beyond governance dashboards.
Boards are not interested in theoretical models. They want assurance that you can elucidate, constrain, and provide evidence regarding what your AI can do to critical systems and data. Framing AI risk as an identity and data question, rather than a vague “AI risk” narrative, enhances the credibility and funding potential of your program.
Questions to Address
If you cannot answer the following questions, your AI program is already outpacing your identity governance:
- Can we present a consolidated list of our material AI systems, including their placement within identity workflows and their operational capacities, along with owners, scopes, and risk tiers?
- Where do AI systems currently possess write or admin-level powers, and who explicitly approved the transition from “assist” to “act”?
- How do we detect and respond when an AI identity’s access expands or its behavior alters in ways that could impact security, compliance, or availability?
- If regulators or auditors request proof that AI identities are governed like other high-risk accounts, what materials could we provide beyond a “responsible AI” presentation?
Organizations poised for success with AI in the coming years will not only be the fastest movers but those who fully understand the limits of their control over who—or what—is permitted to interact with critical systems.
Identity governance sets the speed limit for both types of AI: the AI embedded in your decision fabric and the AI that now acts as digital staff. It also generates the proof that enables boards, regulators, and customers to continue endorsing your AI initiatives as they expand within your business.
