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02/08/2026 12:21 pm
TL;DR: Agentic AI changes the attack surface by introducing planning, tool use, memory, and retrieval into workflows, making single-turn prompts and classic web-app testing insufficient, according to Straiker. The right red-team approach now has to measure end-to-end impact across the runtime, not just model output.
NHIMG editorial — based on content published by Straiker: AI red teaming vs. traditional red teaming: what security teams need to know
Questions worth separating out
Q: How should security teams test AI agents that can call tools and APIs?
A: They should test the agent's actual execution paths, not just its text responses.
Q: Why do traditional AppSec tools fall short for agentic AI?
A: Traditional AppSec tools fall short because they are designed to inspect code, requests, or dependencies, not the meaning of an evolving tool chain.
Q: What do enterprises get wrong about AI red teaming maturity?
A: Many teams stop at attack simulation and assume the test itself is the control.
Practitioner guidance
- Define the agent runtime as a governed access boundary Map every tool, memory store, retrieval source, and external API the agent can touch, then assign explicit ownership for each boundary.
- Test for multi-step business impact Design red-team scenarios that begin with prompt influence but end with a measurable outcome such as unauthorized posting, data movement, or policy bypass.
- Separate model hygiene from runtime governance Use single-turn checks only for early model risk screening, then add agent-aware testing for tool calls, orchestration, and memory persistence.
What's in the full article
Straiker's full blog covers the operational detail this post intentionally leaves for the source:
- Side-by-side comparison of autonomous agentic red teaming, retrofit AI testing, and traditional offensive security coverage
- Traceability details for prompt history, tool calls, retrieved content, and intermediate reasoning used in each finding
- The Chain of Threats forensic view and attack-success reporting used to prioritise remediation
- Practical examples of how runtime guardrails and testing outputs can be paired in a single workflow
👉 Read Straiker's analysis of AI red teaming versus traditional pentesting →
AI red teaming and traditional pentesting: where the gap really is?
Explore further
02/08/2026 1:22 pm
Agentic AI red teaming is now an identity-governance problem, not only a testing discipline. Once an AI system can call tools, access memory, and reach into business systems, it starts to behave like a non-human identity with delegated authority. That changes the security model from output inspection to permission governance. The practical conclusion is that IAM and NHI teams need to treat agent runtime scope as part of the control plane.
A question worth separating out:
Q: How can organisations decide between traditional and agent-aware testing?
A: Use traditional red teaming for perimeter, infrastructure, APIs, and classic application paths. Use agent-aware testing when the system plans, calls tools, retrieves data, or writes to memory. Most programmes need both, but the agentic layer requires its own governance, evidence, and remediation workflow.
👉 Read our full editorial: AI red teaming needs agent-aware coverage, not classic pentest reuse
