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02/08/2026 12:26 pm
TL;DR: AI Security Posture Management is positioned as a continuous control layer for AI systems because traditional CSPM and DSPM do not cover prompt injection, shadow AI, runtime misuse, or model-specific governance gaps, according to Akto. The practical takeaway is that AI security now depends on discovering AI assets, constraining tool access, and monitoring behaviour as a governance problem, not just a deployment problem.
NHIMG editorial — based on content published by Akto: AI Security Posture Management (AI-SPM) complete guide for AI agent security in 2026
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.
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 risks that CSPM and DSPM do not fully cover?
A: CSPM focuses on infrastructure settings and DSPM focuses on data visibility, but AI agents change behaviour through prompts, tools, and memory.
Q: What breaks when AI controls stop at pre-deployment testing?
A: Pre-deployment testing cannot stop a compliant model from making risky decisions in a live workflow or through connected tools.
Practitioner guidance
- Inventory every AI asset and connector Build a live inventory of models, agents, MCP servers, plugins, datasets, and API integrations.
- Scope agent permissions to task-level access Assign each AI agent the smallest set of actions and data sources required for its job.
- Monitor prompts, tool calls, and outputs in production Send AI runtime telemetry into SIEM or SOC workflows so prompt injection, unsafe tool use, and data leakage can be investigated quickly.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step AI-SPM workflow examples for discovery, runtime monitoring, and guardrail enforcement.
- Implementation detail on how Akto Argus probes prompt injection, unsafe tool execution, policy bypass, and sensitive data exposure.
- Practical guidance on integrating AI-SPM with cloud-native environments, SIEM workflows, and CI/CD pipelines.
- Examples of how the vendor maps AI-SPM to agent identity security and enterprise deployment patterns.
👉 Read Akto's complete guide to AI Security Posture Management →
AI-SPM and agentic AI security: what IAM teams need to know?
Explore further
02/08/2026 1:25 pm
AI-SPM is becoming the governance layer that turns AI behaviour into an identity problem. Once an AI system can call tools, touch data, and act without a human approving every step, it starts to resemble a non-human identity with runtime authority. That shifts the governance burden from simple model inventory to access boundaries, permission scope, and revocation discipline. Practitioners should treat AI-SPM as part of identity control, not just AI hygiene.
A question worth separating out:
Q: Which frameworks should organisations use for AI-SPM and agent governance?
A: Start with NIST AI RMF for governance, OWASP Agentic AI Top 10 for application risks, and CSA MAESTRO where multi-agent threat modelling is needed. If the agent acts like a non-human identity, add IAM and NHI governance controls so access, ownership, and revocation are explicit and auditable.
👉 Read our full editorial: AI-SPM is becoming a baseline control for agentic AI security
