Notifications
Clear all
Topic starter
15/08/2026 6:06 pm
TL;DR: AI security best practices now center on identity, least privilege, and runtime monitoring because AI systems create dynamic attack surfaces and a single compromised API key can expose entire training datasets, according to Obsidian Security. The core issue is that security programmes still assume stable, reviewable access while AI agents act with broad, shifting privileges across cloud and SaaS systems.
NHIMG editorial — based on content published by Obsidian Security: AI Security Best Practices: Building a Foundation for Responsible Innovation
By the numbers:
- AI related security incidents cost enterprises an average of $4.88 million per breach, with recovery times extending 38% longer than traditional attacks.
- A 2024 OWASP study found that 67% of deployed LLM applications contained at least one exploitable prompt injection vulnerability.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
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 a different access-risk profile than traditional applications?
A: AI agents can chain actions, call multiple tools, and change behaviour based on context, so one credential can enable more than one operational path.
Q: What breaks when AI agent access is not re-evaluated in real time?
A: The main failure is privilege drift.
Practitioner guidance
- Assign each AI agent a unique identity Bind every agent to a distinct non-human identity, never a shared API key or generic service account.
- Move authorization to runtime policy Evaluate each agent request against task scope, data sensitivity, and current risk rather than relying only on preassigned roles.
- Shorten credential lifetime aggressively Use task-scoped, short-lived credentials for AI platforms and service accounts, with immediate revocation paths for abnormal behaviour.
What's in the full article
Obsidian Security's full article covers the operational detail this post intentionally leaves for the source:
- Step-by-step control guidance for AI agent identity, least privilege, and token lifecycle management.
- Specific monitoring patterns for prompt injection, anomalous tool use, and runtime data access.
- Implementation examples for authentication, authorization, and compliance mapping across AI platforms.
- Practical deployment detail for integrating AI logs with SIEM and SOAR workflows.
👉 Read Obsidian Security's analysis of AI security best practices for enterprise agents →
AI agent identity controls: are your access models keeping up?
Explore further
15/08/2026 6:15 pm
AI agent governance is becoming an identity control problem before it is a model risk problem. The article makes clear that AI systems do not fail only because outputs are unsafe. They fail because identities, tokens, and access paths are easier to abuse than the models they protect. For IAM and NHI teams, that means governance starts at identity issuance, not at model review.
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, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface.
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface.
A question worth separating out:
Q: Who is accountable when an AI agent accesses sensitive data it was not meant to use?
A: Accountability sits with the team that approved the agent, its connectors, and its policy boundaries, not with the runtime behaviour alone. Organisations need ownership for intent, permissions, monitoring, and validation so they can prove whether the agent stayed inside its approved purpose. Without that, audit and regulatory response become retrospective guesswork.
👉 Read our full editorial: AI agent security best practices expose the identity gap
