Topic starter
10/06/2026 12:52 am
TL;DR: Agentic purple teaming compresses red teaming and blue teaming into a continuous loop for generative AI, with autonomous agents simulating attacks and triggering remediation in the same platform, according to Lasso Security. The real shift is that AI security is becoming runtime governance, not periodic review, because static guardrails cannot keep pace with prompt injection, data leakage, and agentic workflows.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Agentic Purple Teaming: A New Strategic Agentic AI Security Solution”.
Key questions
Q: Where does AI security testing fail when teams rely on static guardrails?
A: It fails when the control assumes AI behaviour is stable long enough for periodic review.
Q: Why are AI gateways not enough to stop prompt injection and data leakage?
A: AI gateways control where traffic goes, but they do not understand what the traffic means.
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.
Practitioner guidance
- Define AI attack scenarios around real workflow paths Build simulations around prompt injection, jailbreaks, data leakage, and manipulated tool use in the actual copilots and agent flows you run today.
- Tie findings to immediate policy enforcement Make sure a discovered weakness can trigger masking, blocking, or context-based access tightening without waiting for a later review cycle.
- Monitor live AI outputs for abnormal access behaviour Track unexpected disclosures, unusual access to sensitive data, and repeated policy violations as operational signals, not just audit events.
Bottom line: Agentic purple teaming matters because it merges offensive simulation and defensive enforcement into one runtime cycle for AI systems.
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This topic was modified 3 hours ago by NHI Mgmt Group
11/06/2026 2:26 am
Closed-loop AI security is becoming the new baseline for identity governance. Periodic testing is too slow when copilots and agents can touch data, tools, and APIs in real time. The practical shift is from review-driven assurance to continuous control validation, which is why AI security is now an identity problem as much as a model problem. Practitioners should treat runtime enforcement as the minimum viable control surface.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who should be accountable when autonomous remediation changes AI controls automatically?
A: Accountability should sit with the team that owns the policy, the trigger conditions, and the audit trail for the automated action. If remediation can run without manual review, the organisation still needs a named owner for what the agent is allowed to change, when it can change it, and how evidence is preserved.
👉 Read our full editorial: Agentic purple teaming raises the bar for AI security testing
12/06/2026 4:00 am
Agentic purple teaming turns AI security into runtime governance. The article’s central contribution is not a new testing label but a different operating model. When simulated attacks and remediation happen in one continuous workflow, the control problem shifts from periodic assurance to live policy enforcement. For AI programmes, that is the boundary between testing a system and governing behaviour while it is active.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between red teaming for AI and blue teaming for AI?
A: Red teaming for AI simulates attacks to expose weaknesses in models, prompts, and agent workflows. Blue teaming for AI monitors live behaviour, detects anomalies, and applies containment or remediation. The two serve different purposes, and the article’s point is that AI security is strongest when they operate as a closed loop rather than isolated functions.
👉 Read our full editorial: Agentic purple teaming raises the bar for AI security testing
This post was modified 3 hours ago by NHI Mgmt Group