Topic starter
02/08/2026 11:49 am
TL;DR: 97% of organisations had Gen AI security issues in the past year, Capgemini data cited by Novee shows. AI systems need continuous adversarial testing because updates, prompts, retrieval, and agent actions can all change what controls catch.
Editorial analysis by NHI Mgmt Group, based on content published by Novee: “How AI Red Teaming is Reshaping Modern Defense Strategies”.
By the numbers:
- 97% of organisations surveyed had experienced security issues tied to Gen AI in the past year, according to Capgemini research cited by Novee.
- 66% were concerned about data poisoning and sensitive data leaking through training datasets, according to Capgemini research cited by Novee.
Key questions
Q: Why do AI systems need continuous security testing after launch?
A: AI systems change over time as models, prompts, integrations, and access paths evolve.
Q: What are the signs that an AI model is too risky to deploy?
A: Warning signs include a high jailbreak failure rate, weak prompt injection resistance, frequent hallucinations, unsafe content generation, and unstable responses when prompts are manipulated.
Q: How should security teams use AI red teaming results in production governance?
A: Security teams should treat red team results as control evidence, not as a finished deliverable.
Practitioner guidance
- Define adversarial test scope for AI systems Specify whether the red team is testing the model, prompt layer, retrieval system, agent workflow, or the full stack.
- Retest after every material model change Treat model updates, fine-tuning, new retrieval sources, and tool permission changes as security events.
- Build runtime monitoring for behavioural anomalies Look for unexpected policy overrides, abnormal retrieval patterns, repeated refusal bypass attempts, and agent actions that drift from intended objectives.
Bottom line: AI systems fail in ways that conventional security controls do not reliably detect, especially when attackers manipulate meaning, retrieval context, or delegated actions.
Glossary terms: AI Red Teaming, Semantic Attack, Model Drift, Agentic workflow
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 2 days ago by NHI Mgmt Group
02/08/2026 12:58 pm
AI red teaming has become a control validation discipline, not a specialist exercise. The article shows that AI risk is no longer confined to research labs or model teams, because attackers can work through prompts, retrieved content, and agent workflows. That makes continuous adversarial testing a governance requirement for organisations deploying models into production. The practitioner conclusion is straightforward: if the AI can reason or act, it must also be tested under adversarial conditions.
A question worth separating out:
Q: How should organisations govern external tools used by AI agents?
A: Organisations should review external tools as security inputs, not convenience features. Each tool needs ownership, approval, metadata inspection, and ongoing monitoring for hidden instructions or unexpected behaviour. If an AI agent can act on a tool, then the tool’s provenance and control status should be governed like any other sensitive integration.
👉 Read our full editorial: AI red teaming is exposing the limits of traditional security