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05/07/2026 6:57 pm
TL;DR: AI red teaming simulates adversarial prompts, jailbreaks, data extraction, and model evasion to expose failures in models, applications, and agents before production exposure, according to TROJ.AI. The governance issue is broader than testing quality: security teams need continuous, lifecycle-aware controls that account for changing model behaviour and agentic risk.
NHIMG editorial — based on content published by TROJ.AI: AI Security What Is AI Red Teaming?
Questions worth separating out
Q: How should security teams red team AI systems that can use tools?
A: Security teams should test the full runtime path, not just the model’s text output.
Q: Why do AI systems require different security testing than traditional software?
A: AI systems can fail through interaction, retrieval, and probabilistic behaviour rather than only through code defects.
Q: What breaks when AI red teaming is treated as a one-time exercise?
A: A one-time test misses behavioural drift, new integrations, changing prompts, and expanding tool access.
Practitioner guidance
- Build adversarial test cases for AI workflows Create prompt injection, jailbreak, and leakage scenarios for every AI path that accepts external text, retrieved content, or user uploads.
- Test delegated tool access, not just model output Map which APIs, workflows, and data stores an AI agent can reach, then red team the full execution chain for unintended side effects.
- Re-run security tests after behavioural changes Treat prompt updates, retrieval changes, fine-tuning, and new integrations as security events that require retesting.
What's in the full article
TROJ.AI's full blog post covers the operational detail this post intentionally leaves for the source:
- Specific examples of prompt injection, jailbreak, and leakage test scenarios that practitioners can adapt to their own AI stack
- The article's step-by-step breakdown of how red team findings are prioritised and turned into remediation work
- Guidance on when to retrain a model versus when to apply downstream guardrails and access controls
- The vendor's explanation of how continuous AI red teaming fits into the development lifecycle
👉 Read TROJ.AI's AI red teaming guide for models, applications, and agents →
AI red teaming and agent governance: are controls keeping up?
Explore further
05/07/2026 7:21 pm
AI red teaming is becoming a control test for governance assumptions, not just a security exercise. The article shows that modern AI systems fail in interaction, not only in code. That means governance has to account for prompts, retrieved data, tool use, and behaviour drift as part of the control surface. For practitioners, the key shift is from testing whether a model is safe in theory to testing whether its operating context stays governable in practice.
A few things that frame the scale:
- 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, according to The State of Non-Human Identity Security.
- Lack of credential rotation is cited as the top cause of NHI-related attacks by 45% of organisations, followed by inadequate monitoring and logging at 37%, according to The State of Non-Human Identity Security.
A question worth separating out:
Q: Who should own governance for AI models and agents that affect access decisions?
A: Ownership should sit with the teams that govern risk, identity, and security outcomes together, not with model development alone. When an AI system influences access, fraud, or workflow execution, IAM, PAM, and AI security stakeholders need a shared control model with clear accountability for approval boundaries and lifecycle change.
👉 Read our full editorial: AI red teaming is exposing gaps in model and agent governance
