TL;DR: AI agents are moving into operational workflows faster than most organisations can track them, and Collibra argues that continuous visibility, signals, exception-based response and automated intervention are now necessary to keep AI under control. The deeper issue is that governance models built for static inventories cannot manage systems that change and act in motion.
At a glance
What this is: Collibra argues that AI agent governance breaks when control is still designed around static inventories instead of live systems, because agents change, act and drift faster than manual oversight can follow.
Why it matters: IAM and security teams need continuous visibility and exception-based control for agentic AI because traditional governance cadences cannot keep up with runtime behaviour or delegated action.
Context
AI agent governance means the controls, visibility and response mechanisms used to keep agent behaviour aligned with policy as agents enter business workflows and take action. The governance gap appears when organisations deploy agents faster than they can observe, trace and intervene across the full operating context.
The article describes a shift from experimentation to operational use, where agents are embedded into processes and expected to act across business systems. That makes governance a live operating problem for identity and access teams, not a post-deployment review exercise.
The core issue is not whether AI can be built, but whether it can be commanded once it is already in motion. For organisations scaling agentic AI, that is now a mainstream governance challenge rather than an edge case.
Key questions
Q: How should security teams secure AI agents before they are embedded into business workflows?
A: Security teams should treat AI agents as a distinct identity and access problem, not just an application feature. The first step is to inventory agent activity, define allowed actions, and constrain access to only the data and systems each agent needs. Governance should include monitoring, policy enforcement, and review of agent decisions so unintended actions are detected early.
Q: Why do traditional identity and governance controls miss the biggest risks in agentic AI?
A: Traditional controls are usually designed to manage logins, models, or data assets, not the full sequence of agent actions in real time. That leaves prompt injection, data exfiltration through legitimate looking tool calls, and unsafe multi step behavior outside the main control plane. The risk is in what the agent does across systems, not just where it authenticates.
Q: What are the signs that AI governance is failing in the enterprise?
A: Common warning signs include rapid growth in AI use without matching policy coverage, sensitive files being copied into personal accounts, and a large share of AI apps carrying high or critical risk. Another indicator is weak visibility into who is using which tools and what data they are sending. If teams cannot answer those questions, governance is not working as intended.
Q: Should organisations prioritise identity governance before expanding agentic AI?
A: Yes. Organisations should establish ownership, least privilege, monitoring, and revocation for machine identities before broadening agentic AI use. Without those controls, each new agent can multiply blast radius and create shadow access that is hard to unwind after an incident.
Technical breakdown
Continuous visibility for AI agents and connected systems
AI governance fails when organisations treat models, data and agents as separate inventories instead of one connected runtime. The article describes a landscape where a model is tracked in one place, data in another and business context somewhere else, which means risk only becomes visible after behaviour changes. Continuous visibility is the ability to know what agents exist, what they touch and how they relate to upstream data and downstream workflows in real time.
Practical implication: build a live control view that links each agent to its data sources, workflows and business use cases.
Signals, drift and exception-based response
Signals are the governance layer that turns raw activity into actionable awareness. In this context, a signal is not just an alert, but a contextualised indication that agent behaviour, model performance or system relationships have changed enough to matter. That matters because AI systems often fail silently, with drift appearing gradually in decisions or automated actions rather than in a single obvious incident.
Practical implication: define signal thresholds around meaningful behavioural deviation, not around static inventory change alone.
Why AI control must move from checkpoints to runtime
The article’s central mechanism is that control loops built for fixed review points cannot govern systems that adapt continuously. When an agent can change behaviour as data shifts and workflows evolve, governance has to travel with the system rather than follow it. That is especially important for autonomous behaviour, where the system is making decisions in motion and the window for human review is already closing.
Practical implication: shift governance from periodic review to runtime containment, escalation and constrained intervention.
NHI Mgmt Group analysis
Continuous AI governance is becoming a runtime identity problem, not a documentation problem. The article is describing a control model that breaks because organisations still govern AI as though it were a static asset set. Once agents are embedded in workflows, the challenge becomes who can observe, constrain and intervene while the system is acting. The practical conclusion is that governance must be designed as an operating control, not a record-keeping function.
Control loops that depend on manual review will always lag agentic systems. The article’s point about managing by exception is really a statement about scale and timing. Manual oversight can work for isolated use cases, but it cannot keep up when agents, datasets and models are changing continuously across business processes. Practitioners should treat exception handling as the only sustainable governance pattern at scale.
Active AI governance depends on connected context, not isolated telemetry. The article makes clear that knowing an agent changed is not enough if the change cannot be linked to its data source, business process and downstream effect. That linkage is the difference between reacting to noise and governing actual risk. For identity teams, the takeaway is that traceability across the AI operating chain is now a control requirement, not a nice-to-have.
Autonomous behaviour collapses the assumption that access remains stable long enough to be reviewed. Access review processes were designed for conditions where privileges persist across an observable cycle. That assumption fails when agents adapt, trigger actions and alter their operating scope in motion, because the control window shrinks to runtime. The implication is that governance has to move from retrospective certification to live authorisation and containment decisions.
AI governance is shifting from deployment approval to operational command. The article’s strongest signal is that success is no longer measured by how many agents are deployed, but by how well they are controlled once they start acting. That changes the discipline from project governance to continuous operational stewardship. Practitioners should read this as a mandate to align identity, observability and response around live execution.
What this signals
AI agent governance will increasingly be judged by runtime control rather than policy completeness. The article points to a shift in expectation: organisations will need evidence that they can see agent behaviour as it happens and intervene while it still matters. That makes live observability part of the governance baseline, not an advanced maturity layer.
Exception-based control is the practical bridge between scale and safety. As agent deployments multiply, teams cannot review every action or every change. The operating model that will hold up is one where signals surface only meaningful deviations and response focuses on those exceptions, which is how identity and security teams preserve both speed and control.
For practitioners
- Map every agent to a live system inventory Track agents, models, datasets and workflows in one continuously updated view so teams can see how changes propagate across the operating chain.
- Define behavioural signals for material drift Set thresholds for unexpected agent actions, model instability and relationship changes so governance triggers on meaningful deviation rather than on schedule.
- Shift oversight from reviews to exception handling Focus governance attention on cases where risk, impact or behaviour materially changes, and avoid trying to manually inspect every action.
- Prepare runtime intervention paths Make sure teams can constrain behaviour, adjust inputs, or limit agent influence while the system is operating, not only after a review cycle closes.
Key takeaways
- AI agent governance fails when control loops are still built for static inventories while the underlying systems are changing in motion.
- The operational risk is silent drift, where agents, data and workflows evolve faster than teams can trace their impact.
- Practitioners need live visibility, contextual signals and runtime intervention paths if they want governance to keep pace with deployment.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agents acting in workflows create identity and privilege control problems at runtime. |
| Recommendation — Map agent governance to ASI03 and constrain authority whenever agents can take action across business processes. | ||
| NIST AI RMF | GOVERN — AI Governance and Accountability | The article is about organisational AI governance operating in production. |
| MANAGE — AI Risk Management | The article centres on managing AI risk as systems change during operation. | |
| Recommendation — Use GOVERN to assign accountability for continuous oversight of AI systems in motion. Use MANAGE to align runtime controls, response paths and escalation for changing AI behaviour. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | Continuous authorisation and constrained action are central to the governance problem described. |
| DE.CM-09 — Monitoring for Anomalies and Events | The article depends on detecting behavioural change in production. | |
| Recommendation — Apply PR.AA-05 to keep AI and agent permissions aligned with current operating context. Use DE.CM-09 to monitor agent behaviour for drift and operational anomalies in real time. | ||
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.
- Continuous visibility: The ability to observe control operation, exceptions, and remediation as they happen across systems. In identity and governance programmes, continuous visibility means teams can trace who approved what, what changed, and whether the control actually executed without waiting for a later review cycle.
- Signal: A signal is an actionable message that indicates a relevant change and points to a next step. In agentic workflows, it is more than telemetry because it carries intent, priority, or required response, allowing downstream agents to act without parsing full environment state.
- Managing by exception: An operating model where teams focus governance effort only on changes that materially increase risk or affect outcomes. For AI systems, this is the practical alternative to reviewing everything manually, which does not scale once agents act continuously.
Deepen your knowledge
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Published by the NHIMG editorial team on June 23, 2026.
Updated on October 11, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org