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02/08/2026 11:22 am
TL;DR: Autonomous AI agents now plan, call tools, and act inside sensitive environments, which makes visibility, ownership, and runtime control central to governance, according to Akto. Security can keep an agent uncompromised, but only governance answers who is accountable when the agent acts within its permissions and still causes harm.
NHIMG editorial — based on content published by Akto: Agentic AI Security & Governance: A Complete Enterprise Framework
Questions worth separating out
Q: How should security teams govern agentic AI as it moves into production?
A: Security teams should govern agentic AI as a class of non-human identity, not as a generic application feature.
Q: Why do AI agents increase IAM and PAM risk?
A: AI agents increase IAM and PAM risk because they can execute actions quickly once privilege is available, which shortens the time available to detect misuse.
Q: What do teams get wrong about multi-agent workflows?
A: They often govern each agent separately and miss the workflow-level privilege chain.
Practitioner guidance
- Inventory every agent and connected tool Build a real-time inventory of AI agents, MCP servers, and downstream tools across cloud and endpoint environments, then reconcile that view against approvals and owners.
- Assign named ownership before deployment Create explicit business, technical, security, and executive ownership for each agent before it enters production.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step guidance for defining business, technical, security, and executive ownership for agents.
- Maturity model checkpoints for moving from visibility to continuous oversight in production environments.
- Metric definitions for tracking agent inventory coverage, policy compliance, and security review completion.
- Governance patterns for multi-agent workflows, including shared responsibility and permission inheritance.
👉 Read Akto's full framework for agentic AI security and governance →
Agentic AI security governance: what is your team actually doing?
Explore further
02/08/2026 12:40 pm
Agentic AI governance is becoming an identity problem, not just an AI problem. Once agents can act, they resemble non-human identities that need ownership, scope, and lifecycle control. That makes the governance question as important as the security question, because a technically secure agent can still cause harm if nobody is responsible for its actions. Practitioners should align agent oversight with IAM and PAM discipline, not leave it in an isolated AI program.
A question worth separating out:
Q: How do organisations know if agentic AI governance is actually working?
A: Look for three signals: access decisions tied to task context, complete audit records linking agents to datasets, and rapid revocation when scope changes. If reviewers still need manual reconstruction after an incident, the programme is not mature. Effective governance produces explainable access, not just allowed or denied results.
👉 Read our full editorial: Agentic AI security and governance need continuous ownership
