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02/08/2026 11:22 am
TL;DR: AI agents can take actions across multiple systems, evolve after deployment, and blur accountability, so point-in-time appsec controls no longer hold, according to Akto’s analysis. The governance problem is now operational: enterprises need ownership, inventory, policy, monitoring, and revalidation to keep agent behaviour inside bounded risk.
NHIMG editorial — based on content published by Akto: AI Agent Security Best Practices: How to Secure Enterprise AI Agents
By the numbers:
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps.
Questions worth separating out
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.
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.
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.
Practitioner guidance
- 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.
- Build a complete agent and tool inventory Continuously discover approved, internal, third-party, and shadow agents across cloud, endpoints, browsers, and internal systems.
- 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.
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.
👉 Read Akto's analysis of AI agent security best practices and enterprise controls →
AI agent security best practices: what controls are teams missing?
Explore further
02/08/2026 12:40 pm
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.
A question worth separating out:
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.
👉 Read our full editorial: AI agent security best practices hinge on ownership and runtime controls
