AI governance gaps in agentic systems expose identity risk

At a glance

What this is: This webinar analysis argues that AI governance is being outpaced by agent adoption, creating identity and security risk where workflows, decisions, and controls are increasingly shared with AI systems.

Why it matters: It matters because IAM, PAM, and GRC teams now have to govern agent access and machine identity risk as part of the core security programme, not as a separate AI policy exercise.

Context

AI governance is the control layer that sets rules, oversight, and accountability for how AI systems are used. In this article, the gap is not theoretical. Delinea argues that organizations are embedding AI systems and agents across workflows, customer interactions, products, and decision-making faster than governance can keep up.

For identity practitioners, the important shift is that AI governance now intersects directly with access management, privileged controls, and compliance oversight. Once agents can act inside operational workflows, the question becomes who governs their permissions, reviews their use, and owns the risk when those identities scale faster than policy.

This is an agentic AI governance problem, but it also reaches into traditional IAM and PAM programmes because machine identities are becoming part of the decision path. The article is framed as a webinar, so the value lies in the operational implications rather than a single vendor feature.

Key questions

Q: What breaks when an AI agent is deployed without formal ownership?

A: When an AI agent has no formal owner, review, offboarding, and incident response all become slower and less reliable. No one is accountable for permission drift, stale credentials, or unexpected behaviour, so the identity can persist long after the original use case has ended. That is a lifecycle failure, not just an administrative oversight.

Q: Why does unmanaged agent access create compliance and security risk?

A: Because an agent can act inside business processes, the risk is no longer limited to what the model knows. It becomes a question of what credentials it uses, what systems it can touch, and whether those actions are governed, reviewed, and evidence-backed. Compliance teams care because the organisation may be unable to demonstrate control over decisions made through the agent.

Q: What are the signs that AI governance controls are not keeping pace with adoption?

A: Common warning signs include unclear ownership for AI use cases, inconsistent approval processes, limited visibility into where sensitive data enters models, and weak evidence for audits or assessments. Teams also struggle when privacy, security, and legal review happen late or manually, because that usually means governance is reactive rather than embedded in the AI delivery process.

Q: How do security teams align AI governance with existing IAM and data security programmes?

A: Security teams should align AI governance with existing IAM and data security programmes by mapping every AI workflow to an accountable identity, a sensitive-data classification, and a logging requirement. That keeps oversight inside current operating models instead of creating a detached AI exception process. The result is faster control adoption and clearer auditability.

Background and context

Why agentic systems create a governance gap

Agentic systems are different from conventional automation because they can participate in decisions, workflows, and interactions rather than simply execute a fixed script. That changes the governance problem. Policy written for static systems assumes that access can be reviewed, scoped, and controlled around stable processes. When agents are embedded into products and workflows, governance has to follow the identity boundary of the agent, not just the application that hosts it. The risk is unmanaged expansion of authority across business processes, especially when the organization treats AI adoption as a tooling issue instead of an access-control issue.

Practical implication: map every agent to an explicit identity, owner, and permission boundary before it reaches production.

How machine identities enter the AI control plane

Machine identities are the credentials and trust relationships that let non-human actors authenticate and operate inside enterprise systems. In agentic environments, they become the practical enforcement point for what an agent can see, call, and change. That makes them part of governance, not just infrastructure. If the organization cannot inventory those identities, assign them to owners, and align them with policy, then AI governance becomes an exercise in post-hoc documentation. The article’s core warning is that risk grows when machine identity control is fragmented across security, operations, and application teams without a shared governance model.

Practical implication: include machine identities in the same governance register used for privileged human and service access.

Why existing GRC and security programmes fall short

Existing GRC and security programmes often assume that systems are either human-operated or fixed-function. Agentic AI breaks that assumption because the identity may initiate actions, chain tasks, and touch multiple business processes without a human in the loop. That creates a gap between policy intent and runtime behaviour. Security leaders may have controls for software, users, and third-party access, but still lack a review model for autonomous or semi-autonomous agent behaviour. The article frames governance as a business requirement because the old model does not naturally account for this kind of runtime authority.

Practical implication: extend access review and risk assessment processes to cover AI-driven actions, not just user accounts.

NHI Mgmt Group analysis

AI governance has become an identity problem, not a policy problem. Once agents are embedded in workflows and decision-making, the question is no longer whether the organization has an AI policy on paper. The real issue is whether identities, permissions, and oversight exist at the point where the agent acts. That shifts the centre of gravity from ethics language to governance mechanics, and practitioners should treat agent access as a control surface.

Machine identities are now part of the governance boundary. AI systems do not operate in isolation; they act through credentials, tokens, roles, and access paths. If those identities are not inventoried and governed with the same discipline used for privileged human access, the organisation creates unmanaged authority inside business processes. The implication is that IAM and PAM teams cannot stay downstream of AI programmes anymore.

Responsible AI governance will increasingly be judged by operational evidence. Leaders will not be asked whether they support AI governance in principle. They will be asked whether they can show ownership, access scope, review cadence, and exception handling for agents already in production. That makes the governance gap measurable, which is why the programme needs to move from aspiration to control execution.

AI governance programs that ignore agent sprawl will misread the risk curve. Adoption can outpace policy maturity quickly, especially when business teams see agents as a productivity shortcut. That creates shadow AI conditions even in otherwise mature enterprises. Practitioners should expect governance demand to rise first in security, compliance, and IAM teams, not in the AI project office.

Runtime authority is the concept to watch. Agentic systems matter because the risky moment is not model training or abstract policy drafting, but the runtime decision to act, call tools, and cross a trust boundary. Governance has to track that runtime authority directly, or it will arrive too late to matter. The practical conclusion is that access governance must move closer to execution time.

From our research library:

• 52% of respondents see AI security decision-making power shifting toward platform and infrastructure teams rather than the executive suite, according to the 2026 Infrastructure Identity Survey.
• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
• Read next: Agentic AI Identity Risk Board Briefing

What this signals

Agent sprawl will force governance closer to execution time. The organizations most exposed are not the ones asking whether AI governance matters, but the ones allowing agents to move into workflows without a control owner. Governance will increasingly have to prove runtime authority, not just policy existence.

Governance failure becomes visible when identity controls and AI controls are managed separately. AI policy, IAM, PAM, and GRC cannot remain disconnected if agents are already taking action in enterprise systems. The operating model has to follow the identity boundary of the agent, or risk review will always lag actual behaviour.

Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey. That gap shows why leaders should expect pressure to formalise agent governance before business adoption outruns control coverage.

For practitioners

• Define agent ownership and accountability Assign every production AI agent a named business owner, technical custodian, and risk owner before it is allowed to participate in workflows or customer interactions.
• Inventory machine identities used by AI systems Create a register of tokens, service accounts, API keys, and other credentials that agents depend on, and tie each one to a business purpose and expiration rule.
• Extend access reviews to agent behaviour Review not only what the agent can reach, but also what actions it actually performs, which systems it touches, and where exceptions are being granted outside normal governance.
• Align AI governance with existing GRC controls Embed AI approvals, exception handling, and evidence collection into current security and compliance workflows so governance does not live outside the operating model.

Key takeaways

• Agentic AI turns governance into an identity and access problem because runtime authority now matters as much as the model itself.
• The biggest operational gap is the lack of ownership, inventory, and review over the credentials and permissions agents use in production.
• Security, IAM, and GRC teams need a shared governance model for agents before AI adoption creates unmanaged authority at scale.

Key terms

• Agentic AI: Autonomous AI systems capable of planning, deciding, and taking actions, including calling APIs, writing code, and orchestrating other agents, with minimal human oversight. Agentic AI introduces new NHI risks as agents must authenticate to external services.
• Machine Identity: The digital identity of a machine, device, or workload, such as a server, container, or VM, used to authenticate it within a network. Sometimes used interchangeably with NHI, though NHI is the broader category.
• AI Governance: AI governance is the set of controls used to discover, classify, approve, restrict, monitor, and revoke AI-enabled access. It connects identity, data, and policy so organisations can manage what AI can reach, what it can share, and when it should be stopped.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.