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02/08/2026 12:06 pm
TL;DR: AI systems now blend models, tools, and autonomous workflows, so teams need identity-aware controls rather than model-only scanning. Protect AI and Akto target different layers of AI security, with one centered on model security across the lifecycle and the other on agent and MCP discovery, guardrails, and runtime protection, according to Akto.
NHIMG editorial — based on content published by Akto: Protect AI: Key Features, Security Capabilities & Essential Tools
Questions worth separating out
Q: How should security teams govern AI agents that choose tools at runtime?
A: Security teams should treat runtime tool choice as a governed access event, not a normal application call.
Q: Why do AI agents complicate traditional IAM reviews?
A: Traditional IAM review assumes identities have human lifecycle events such as hire, role change, or offboarding.
Q: What breaks when AI security stops at model scanning?
A: Model scanning helps identify tampering and unsafe dependencies before deployment, but it does not address runtime misuse.
Practitioner guidance
- Map AI agents to their actual credentials and tool reach Identify every agent, MCP connector, and API endpoint in use, then record which credentials, scopes, and data sources each one can touch.
- Separate model review from runtime access review Do not sign off on an AI system only because the model passed pre-deployment testing.
- Enforce least privilege at the connector layer Use policy to constrain which tools an agent can invoke, which data it can retrieve, and which actions require additional approval.
What's in the full article
Akto's full blog post covers the operational detail this post intentionally leaves for the source:
- Side-by-side product feature descriptions for Protect AI and Akto across discovery, runtime monitoring, and guardrails
- More granular discussion of AI agent and MCP discovery coverage across endpoints, connectors, and infrastructure
- Tool-level comparisons for model scanning, red teaming, and runtime protection workflows
- Implementation-oriented product positioning for teams evaluating AI security platforms
👉 Read Akto's comparison of Protect AI and agentic AI security →
Protect AI vs Akto: are AI security controls keeping up?
Explore further
02/08/2026 1:11 pm
AI model security and AI agent governance are not interchangeable control problems. Model scanning can reduce the risk of poisoned inputs, vulnerable artifacts, and unsafe deployment content, but it does not govern what an agent does after deployment. Once the actor can take independent actions across tools and APIs, the relevant question becomes privilege, delegation, and runtime accountability. Practitioners should treat model assurance as one layer, not the operating control for AI behaviour.
A few things that frame the scale:
- 1 in 4 organisations are already investing in dedicated NHI security capabilities, with an additional 60% planning to do so within the next twelve months, according to The State of Non-Human Identity Security.
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities.
A question worth separating out:
Q: How do security teams know runtime AI guardrails are actually working?
A: Look for blocked poisoned inputs, flagged anomalous outputs, and traceable enforcement before responses reach users or downstream systems. If controls only inspect prompts or only inspect outputs, they leave a gap that attackers can exploit through manipulated data sources or tool responses.
👉 Read our full editorial: Protect AI vs Akto: what AI security governance actually changes
