Topic starter
18/08/2026 2:38 pm
TL;DR: AI safety and AI security solve different risks, but both fail when GenAI is deployed faster than testing, guardrails, and observability can keep up, according to ActiveFence. The operational gap is no longer theoretical: harmful outputs, data leaks, and model misuse now create business, legal, and trust exposure at scale.
Editorial analysis by NHI Mgmt Group, based on content published by ActiveFence: “What is AI Safety and Security?”.
Key questions
Q: How should teams govern GenAI when model behaviour changes faster than policy reviews?
A: Use continuous governance, not one-time approval.
Q: Why do static AI safety checklists fail in production?
A: Static checklists fail because they assume model behaviour, user inputs, and abuse tactics stay stable.
Q: What are the signs that GenAI guardrails are not working as intended?
A: Common warning signs include unsafe outputs reaching users, policy violations slipping through, slow or inconsistent enforcement, and operational friction that pushes teams to bypass controls.
Practitioner guidance
- Implement continuous red teaming for GenAI Stress-test prompting paths, model outputs, and abuse cases on a recurring schedule so the control set evolves with the system.
- Instrument guardrail observability Track blocked prompts, unsafe completions, escalation attempts, and policy override patterns so control failures are visible and reviewable.
- Tighten access around model integrations Limit which users, apps, and agents can call the model, retrieve data, or trigger downstream actions, and review those permissions as part of normal governance.
Bottom line: AI safety failures are not solved by deployment-time approval alone, because model behaviour, prompt paths, and abuse tactics keep changing after launch.
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18/08/2026 3:23 pm
Continuous control, not policy intent, is the real dividing line in GenAI governance. The article is correct to frame AI safety and AI security as complementary, but the operational failure mode is relying on best practices as if they were durable controls. In fast-changing model environments, policy only matters when it is enforced at runtime, measured continuously, and adjusted as abuse patterns change. Practitioners should treat control drift as a standing governance risk.
A few things that frame the scale:
- 92% of organisations expose NHIs to third parties, raising concerns about supply chain security, according to the Ultimate Guide to NHIs.
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: Who should own accountability for AI safety controls when models can call tools?
A: Accountability should sit with the team that owns the end-to-end workflow, not with the model itself. Once a model can fetch data, trigger tools, or influence access decisions, the organisation needs a named control owner for both policy enforcement and audit evidence.
👉 Read our full editorial: AI safety and security need continuous controls, not best practices