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15/08/2026 6:12 pm
TL;DR: AI red teaming is emerging as a necessary security discipline because traditional penetration testing misses prompt injection, model inversion, and AI agent workflow abuse, according to Obsidian Security. The practical shift is toward continuous testing, MLOps integration, and governance that treats AI systems as a distinct attack surface, not just another application layer.
NHIMG editorial — based on content published by Obsidian Security: AI Red Teaming: How Enterprises Test and Harden Their AI Systems
By the numbers:
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
- 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: How should security teams handle prompt injection in AI systems?
A: Treat prompt injection as an authorisation problem, not only a content problem.
Q: Why do AI agents increase IAM and PAM risk?
A: AI agents increase IAM and PAM risk because they can execute actions quickly once privilege is available, which shortens the time available to detect misuse.
Q: What do organisations get wrong when they treat AI red teaming as a one-time assessment?
A: They assume the result stays valid after the model, prompts, data connectors, or orchestration logic changes.
Practitioner guidance
- Map AI agent identities and permissions Create a full inventory of every AI agent, service account, token, and API scope used in production workflows.
- Embed red teaming into MLOps gates Run adversarial tests whenever models are retrained, prompts change materially, or new tool integrations are added.
- Measure workflow exposure, not just model accuracy Track which interaction paths are tested, which data sources are reachable, and which actions an agent can execute under stress.
What's in the full article
Obsidian Security's full article covers the operational detail this post intentionally leaves for the source:
- Step-by-step AI red teaming techniques for prompt injection, model inversion, and adversarial input testing.
- Practical guidance on embedding security checks into MLOps and CI/CD workflows without disrupting release cadence.
- Metrics and benchmarks for measuring vulnerability discovery rate, remediation speed, and test coverage across AI systems.
- Platform integration detail for teams that want to correlate AI findings with broader security and governance workflows.
👉 Read Obsidian Security's analysis of AI red teaming for enterprise AI systems →
AI red teaming for enterprise systems: are your controls keeping up?
Explore further
15/08/2026 6:19 pm
AI red teaming is becoming a control discipline, not just a testing exercise. Once AI systems can read data, call tools, and generate actions, the security question is whether those behaviours remain within governed bounds. That pushes red teaming into the same decision space as IAM, PAM, and NHI governance. The practical conclusion is that AI assurance now needs measurable controls, not just occasional adversarial testing.
A question worth separating out:
Q: How should teams account for AI red teaming under governance and compliance obligations?
A: Assign clear ownership, keep audit evidence for test scope and remediation, and connect findings to risk and change management records. AI security testing should support governance decisions, not sit outside them. That gives compliance teams traceability while helping engineering teams fix the specific control gaps that red teaming exposed.
👉 Read our full editorial: AI red teaming exposes the security gaps in enterprise AI systems
