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23/06/2026 9:04 pm
TL;DR: AI agent security is shaped less by model choice than by how organisations govern human authority, delegated permissions, and privileged operations, according to P0 Security. When standing privilege, service accounts, and approval paths are already loose, agentic systems inherit those weaknesses instead of fixing them.
NHIMG editorial — based on content published by P0 Security: Securing AI agents starts with governing human authority
By the numbers:
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: How should security teams govern AI agents that inherit human permissions?
A: They should govern the full delegation chain, not just the agent.
Q: Why do standing privileges make AI agent security harder?
A: Standing privileges make agent security harder because they create persistent authority that an agent can consume immediately at runtime.
Q: What breaks when teams treat agent security as only a model problem?
A: What breaks is the governance boundary.
Practitioner guidance
- Inventory delegated authority paths Map how users, service accounts, workflows, and agents combine permissions today, then identify where a single action can inherit broader authority than the requester should have.
- Reduce standing privilege before agent rollout Remove persistent privileges that are only needed occasionally, especially in operational systems that agents may touch.
- Separate authentication from authorisation decisions Require contextual policy for each agent action so login success does not imply operational permission.
What's in the full article
P0 Security's full article covers the operational detail this post intentionally leaves for the source:
- The specific deployment models for centrally governed agents, user-scoped agents, and workflow-driven agents.
- The runtime distinctions between requester identity, agent identity, and service-account identity in practical environments.
- The exact control questions teams should ask before delegating authority to AI agents.
- The Identiverse booth context and implementation framing that sits outside the governance analysis here.
👉 Read P0 Security's analysis of human authority and AI agent security →
AI agent security and human authority: what governance teams miss?
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25/06/2026 2:12 am
Human authority is now the first control plane for AI agent security. Agent governance does not start at the model layer. It starts with the human access model, because the agent can only operate inside the authority the organisation has already created or allowed to be inherited. That means standing privilege, approval quality, and delegated operational scope determine the real ceiling of agent risk. Practitioners should treat human IAM and PAM hygiene as preconditions for any credible agent programme.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who is accountable when an AI agent uses delegated access incorrectly?
A: Accountability should follow the delegated authority chain, not stop at the agent label. The relevant owners are the teams responsible for the human identity, the service identity, the workflow, and the policy that allowed the action path. If those responsibilities are not explicit, incident review will be incomplete and remediation will focus on the wrong layer.
👉 Read our full editorial: Human authority governs AI agent security before agent controls do
