Topic starter
15/08/2026 6:13 pm
TL;DR: Gartner’s 2026 trends place AI Security Platforms among the technologies enterprises are expected to adopt as AI-native risks outgrow traditional network, endpoint, and identity controls, according to AppSOC’s analysis. The practical issue is not AI adoption itself but the lack of unified visibility, policy enforcement, and testing across third-party AI services and custom AI applications.
Editorial analysis by NHI Mgmt Group, based on content published by AppSOC: “A detailed look at Gartner's definition of AI Security Platforms”.
Key questions
Q: What breaks when organisations rely on endpoint controls alone for AI use?
A: Endpoint-only control misses the in-session behaviour that determines whether AI use is safe or compliant.
Q: Why do AI services and custom AI applications need different security controls?
A: Third-party AI services mainly create exposure through acceptable use, data sharing, and policy compliance, while custom AI applications add risks such as prompt injection, model poisoning, and unsafe agent behaviour.
Q: How can organisations tell whether AI governance is actually working?
A: Organisations can tell AI governance is working when they can inventory every agent, explain its purpose, show who owns it, and prove that permissions are tightly scoped.
Practitioner guidance
- Build a complete AI inventory Identify third-party AI services, internal model deployments, and autonomous agents across business units, then classify them by data sensitivity, business use, and privilege scope.
- Separate usage controls from application controls Apply one control set to manage employee and system use of external AI services, and a second control set to test and secure custom AI applications, models, and agents.
- Add prompt and agent testing to assurance routines Test for prompt injection, unsafe outputs, model artefacts, and agent actions that deviate from policy before AI systems are promoted into production workflows.
Bottom line: AI security platforms are emerging because conventional controls were not built to govern prompt injection, shadow AI, data leakage through LLMs, or rogue agent behaviour.
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15/08/2026 6:19 pm
AI security is now a governance category, not a niche technical add-on. The core issue is not whether organisations will use AI, but whether they can govern AI usage and AI applications as two separate risk surfaces. That split matters because third-party AI services and custom AI systems fail in different ways, and a single control model rarely covers both. Practitioners should treat AI security as a board-visible governance domain with distinct policy, assurance, and monitoring obligations.
A few things that frame the scale:
- 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: Should organisations treat AI prompt security and identity security as separate controls?
A: No. Prompt security limits what the model can generate, but identity security limits what the surrounding system can reach and change. If an AI workflow holds privileged secrets, the practical risk sits in authorisation and credential governance, not in prompt content alone.
👉 Read our full editorial: AI security platforms expose the gap in existing enterprise controls