AI agent security best practices hinge on ownership and runtime controls

At a glance

What this is: This is Akto’s analysis of why AI agent security needs a dedicated governance model, with ownership, inventory, runtime policy, and continuous validation as the core controls.

Why it matters: It matters because AI agents act across systems and change over time, so IAM, NHI, and security teams need controls that govern what agents can do, not just what they can read.

By the numbers:

• 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so.
• 85% of organisations lack full visibility into third-party vendors connected via OAuth apps.

👉 Read Akto's analysis of AI agent security best practices and enterprise controls

Context

AI agent security is the problem of governing software that can decide, act, and adapt across tools and systems rather than simply respond to requests. That changes the control model from static application protection to continuous oversight of identity, access, policy, and behaviour. For IAM and NHI programmes, the key question is not whether an agent exists, but what it is allowed to do and who is accountable when it does it.

The article argues that most failures are operational rather than purely technical. Organisations may have a pilot under control, but they lose visibility once agents multiply across teams, connect to more systems, and drift after launch. That pattern is typical of early enterprise AI adoption and is especially relevant where agents interact with MCP-connected tools, secrets, or high-risk business workflows.

Key questions

Q: How should security teams govern AI agents that can access enterprise systems?

A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring. The control set should include inventory, task-bound credentials, audit trails, and revocation paths. If an agent can call tools or touch production systems, it belongs in the same governance model as service accounts and other machine identities.

Q: Why do AI agents create more risk than traditional automation?

A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously. Traditional automation follows fixed rules, but an agent can be manipulated into using its own authority in unintended ways. That makes permission scope, tool boundaries, and monitoring more important than model accuracy alone.

Q: What do teams get wrong about AI agent discovery?

A: Teams often treat discovery as a one-time inventory exercise, but AI-connected access changes as users add apps, permissions, and workflows. Discovery only has lasting value when it feeds ownership, classification, and recertification. Otherwise the inventory becomes a static list of already outdated identities and permissions.

Q: Who should be accountable when an AI agent causes a security incident?

A: Accountability should sit with the human owner, platform team, or business function that granted and operated the agent. The identity may act independently, but governance cannot detach responsibility from the delegation chain. Programs should define ownership, escalation, and remediation paths before deployment so responsibility is clear when the agent's behaviour changes.

Technical breakdown

Why AI agents break the assumptions behind traditional application security

Traditional application security assumes a system waits for a request, executes a bounded function, and remains stable until the next release. AI agents can choose actions, call tools, and adapt their behaviour as prompts, models, and integrations change. That means security has to govern the action space, not just the data surface. The core technical shift is from static permission review to runtime control over decision-making systems. Practical implication: treat agent permissions and tool access as continuously governed runtime state, not as a one-time deployment setting.

Practical implication: treat agent permissions and tool access as continuously governed runtime state, not as a one-time deployment setting.

AI agent inventory and shadow AI discovery

An AI agent inventory is the authoritative map of approved, internal, third-party, and shadow agents, plus the tools and systems they can reach. Without that map, teams cannot classify risk, assign owners, or prove policy coverage. The article’s focus on MCP servers matters because each agent-tool relationship becomes part of the effective attack surface. Discovery must therefore include endpoints, browsers, cloud services, and internal infrastructure, not only sanctioned platforms. Practical implication: build continuous discovery that identifies both agents and their connected tool chains before you attempt governance.

Practical implication: build continuous discovery that identifies both agents and their connected tool chains before you attempt governance.

Runtime policy enforcement and continuous validation for evolving agents

AI agents are not static assets. Their model, prompts, tools, and upstream dependencies can change after launch, which makes point-in-time testing insufficient. The technical control model needs pre-deployment policy, runtime guardrails, and recurring validation against prompt injection, tool abuse, and privilege escalation. Monitoring then becomes the feedback loop that shows whether policy is actually constraining behaviour in production. Practical implication: enforce least privilege at runtime and re-test whenever an agent’s model, access scope, or connected tools change.

Practical implication: enforce least privilege at runtime and re-test whenever an agent’s model, access scope, or connected tools change.

Threat narrative

Attacker objective: The attacker aims to turn a single agent compromise or policy gap into broad enterprise access and unauthorised business action.

1. Entry occurs when an agent is deployed with access to multiple internal systems and connected tools that were not fully inventoried or approved.
2. Escalation follows when the compromised or misbehaving agent can chain those tool permissions across CRM, data, email, and internal services.
3. Impact is realised when the agent performs out-of-scope actions in real systems, including data exposure, unauthorised system access, or downstream operational change.

NHI Mgmt Group analysis

AI agent governance is becoming an identity problem as much as an application problem. Once an agent can authenticate, call tools, and act across systems, it behaves like a high-risk non-human identity with decision-making capability. That means inventory, ownership, and privilege scope are no longer supporting controls, they are the programme boundary. Practitioners should treat agent identity governance as a first-class control plane, not a sidecar to appsec.

Agent lifecycle drift is the named risk that most teams still underestimate. The article correctly shows that a safe agent on day one can become unsafe after a model swap, tool expansion, or upstream change. That is a governance assumption failure, not a tooling gap. Continuous review and reclassification are therefore essential for any agent that interacts with secrets, customer data, or production systems.

Blast radius is the right concept for AI agents, not simple access count. A single agent may hold permissions across several systems, which means the effective risk is determined by how far one compromise can travel. This makes relationship mapping and least-privilege scoping more important than raw inventory volume. The practitioner conclusion is clear: reduce connected scope before scale multiplies the attack surface.

Ownership is the control that makes every other AI agent safeguard enforceable. Without a named business owner, technical owner, and security owner, policy enforcement becomes advisory and incidents stall in ambiguity. That accountability model aligns with NIST AI RMF GOVERN principles and with OWASP Agentic AI guidance on privilege misuse and tool trust. Organisations should formalise ownership before they allow agents into production.

What this signals

Agent governance debt is now a programme-level risk: as agent counts grow, the weak point is not deployment speed but the absence of inventory, ownership, and usable telemetry. Teams that cannot map connected tools or prove policy enforcement will struggle to contain both security incidents and compliance exposure.

Blast radius control should become a design requirement for AI programmes. The practical test is whether a compromised or misconfigured agent can reach more systems than the business owner intended. If the answer is yes, the programme is scaling access faster than it is scaling control, and that will eventually surface in incident response, audit, or legal review. For identity teams, the next step is to align AI agent governance with NHI lifecycle controls and runtime access boundaries.

The near-term signal is that governance will converge around identity, policy, and runtime monitoring rather than model-centric safeguards alone. Practitioners should expect more emphasis on ownership records, access scoping, and continuous revalidation as AI agents move from pilots into production workflows.

For practitioners

• Assign named ownership for every agent before production Record business, technical, and security owners for each agent, and make approval, review, and incident response dependent on that assignment. If no one can be held accountable, the agent is effectively ungoverned.
• Build a complete agent and tool inventory Continuously discover approved, internal, third-party, and shadow agents across cloud, endpoints, browsers, and internal systems. Include the tools, MCP servers, and data sources each agent can reach so risk analysis reflects reality.
• Enforce runtime policy before agents can act Define data access, usage, and invocation controls before deployment, then block out-of-bounds actions at runtime rather than logging them after the fact. Pair this with least privilege and human-in-the-loop approval for critical actions.
• Revalidate agents whenever scope changes Trigger reassessment after any model change, prompt update, tool addition, or access expansion. Use red teaming and adversarial testing to catch prompt injection, tool abuse, and privilege escalation before production drift becomes an incident.
• Monitor agent behaviour as an operational control Track tool calls, data access, policy violations, and incident patterns in production so security teams can see what an agent actually does. Integrate these logs with review cycles and risk registers to keep governance current.

Key takeaways

• AI agent security fails when organisations treat autonomous systems like ordinary applications with static controls.
• The strongest indicator of maturity is not deployment volume, but whether teams can inventory, own, and constrain every agent in production.
• Runtime guardrails, continuous validation, and clear accountability are the controls that keep agent behaviour inside bounded risk.

Key terms

• AI agent inventory: An AI agent inventory is a complete record of autonomous or semi-autonomous software entities, including their permissions, tools, and reachable resources. It is a governance baseline because teams cannot review, restrict, or remediate agent access until they know exactly what the agent estate contains.
• Runtime Guardrail: A control applied while an AI agent is operating, not just during configuration or review. Guardrails can block dangerous tool calls, require approval for sensitive actions, or stop data leakage before it reaches systems or users.
• Blast Radius: The potential scope of damage if a specific credential or identity is compromised. Identities with broad permissions have a larger blast radius and represent a higher priority for least-privilege enforcement and security controls.
• Agent Lifecycle Drift: Agent lifecycle drift is the gap between an agent's original approved state and its later production behaviour. It happens when models, prompts, tools, or upstream services change after launch, making the agent materially different from the version that was initially reviewed.

What's in the full article

Akto's full post covers the operational detail this post intentionally leaves for the source:

• A seven-step operating model for discovery, approval, deployment, monitoring, and review across enterprise AI agents.
• A practical risk classification table that maps low, medium, high, and critical agents to different control expectations.
• Detailed guidance on ownership roles for business, technical, security, and executive stakeholders.
• Examples of runtime policies, monitoring signals, and reassessment triggers for changing agent behaviour.

👉 Akto's full post covers the AI agent lifecycle, risk tiers, and runtime governance practices in more detail.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It is designed for practitioners who need to connect identity controls to real-world operational risk.