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20/08/2026 5:25 pm
TL;DR: Traditional AI-SPM is built for static models, but agentic AI needs governance over identity, permissions, tool chains, and runtime behaviour because 40-plus agents can operate across cloud accounts without clear ownership or visibility, according to Fiddler. The practical shift is from configuration scanning to runtime observability, because autonomous workflows create security decisions that CSPM and DSPM cannot see.
NHIMG editorial — based on content published by Fiddler: AI Security Posture Management in the Age of Agents
By the numbers:
- 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.
- 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: What should organisations rethink when AI agents can act without human approval?
A: Organisations should rethink review cycles, revocation timing, and accountability assumptions.
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 organisations get wrong about AI posture management?
A: The most common mistake is treating AI posture management as a better version of CSPM.
Practitioner guidance
- Define unique identities for every agent Bind each AI agent to a distinct identity, ownership record, and policy scope so that tool calls and delegated actions remain auditable across runtime sessions.
- Inventory AI workloads as governed assets Maintain a living AI-BOM that records every model, agent, pipeline, dataset, and external service so that shadow AI cannot hide outside approved governance.
- Separate pre-input and post-output controls Use pre-LLM filters to block prompt injection, secrets, and sensitive data before model execution, then apply post-execution review before outputs reach users or downstream systems.
What's in the full article
Fiddler's full blog covers the operational detail this post intentionally leaves for the source:
- A practical breakdown of AI-SPM capability requirements, including AI-BOM inventory, supply chain assessment, and runtime policy enforcement.
- Examples of posture policies for agent identity, tool permissions, execution limits, and telemetry retention across agentic workflows.
- A deeper treatment of coding-agent posture management, including repository scope, shell access, and pre-merge review boundaries.
- The article's discussion of agentic hierarchy and span-level telemetry across multi-agent decision trees.
👉 Read Fiddler's analysis of AI security posture management for agents →
AI security posture management for agents: is runtime control enough?
Explore further
20/08/2026 6:15 pm
AI posture management is becoming identity governance for machines that act. Once an agent can choose tools, inherit access, and produce side effects, it stops behaving like a passive model endpoint and starts behaving like a governed principal. That shifts the control question from model safety to identity assurance, permission scoping, and evidence capture. Practitioners should read AI-SPM as an extension of NHI governance, not as a renamed cloud scan.
A question worth separating out:
Q: When should organisations tighten controls on coding agents?
A: Organisations should tighten controls as soon as a coding agent can read repositories, access terminals, or reach infrastructure-as-code templates. Those capabilities create direct pathways to secrets, deployment changes, and code tampering. The safest default is least privilege, pre-merge review, and explicit limits on shell and repository access.
👉 Read our full editorial: AI security posture management now has to govern agents, not just models
