Topic starter
28/04/2026 4:54 pm
TL;DR: AI-related attacks have increased nearly 490 percent year over year, while around 80 percent of incidents involve regulated or sensitive data, according to Grip Security’s webinar summary. The core issue is not model behaviour alone but identity, access, and integration sprawl inside SaaS environments, where governance lags behind deployment speed.
Editorial analysis by NHI Mgmt Group, based on content published by Grip Security: “Top AI Security Risks in 2026”.
Key questions
Q: What breaks when AI access is managed on the same cadence as human access reviews?
A: Periodic access reviews miss the short-lived but high-impact privilege growth that can happen between reviews.
Q: Why do OAuth integrations increase AI security risk?
A: OAuth turns access into a delegated relationship that can persist across applications and data sets.
Q: How should security teams govern Shadow AI in SaaS applications?
A: Security teams should govern Shadow AI by classifying AI-capable SaaS tools, deciding what data each tool may process, and enforcing those decisions centrally.
Practitioner guidance
- Map AI-related identities across SaaS Build an inventory of users, service accounts, OAuth apps, automations, and embedded AI features that can access business data.
- Audit OAuth permissions for excessive scope Review delegated access grants for scope breadth, tenant reach, and persistence, then remove any integration that can access data beyond its current purpose.
- Discover embedded AI features in trusted SaaS Extend discovery beyond standalone AI tools to include AI functions embedded in CRM, HR, collaboration, and support platforms.
Bottom line: AI risk in 2026 is presented here as a governance problem rooted in access sprawl across SaaS, not as a standalone model issue.
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This topic was modified 52 minutes ago by NHI Mgmt Group
12/10/2026 3:32 pm
AI security is now an identity governance problem with an AI surface, not an AI governance problem with an identity add-on. The article’s core finding is that access, permissions, and integrations are where risk compounds. That means SaaS identity governance has become the control plane for AI exposure, and teams that leave it outside their AI programme are managing the wrong layer.
A few things that frame the scale:
- 76% of organisations cite shadow AI as a definite or probable problem, up from 61% in 2025, according to HiddenLayer's 2026 AI Threat Landscape Report.
A question worth separating out:
Q: How should organisations govern AI systems that operate across identity, data, and runtime layers?
A: Use one policy model that ties identity scope, data access, and runtime enforcement together. If those controls are split across teams or products, the agent can move faster than the governance process and exploit the gaps between layers. Cross-platform oversight matters because AI risk is usually an interaction problem, not a single-control failure.
👉 Read our full editorial: Top AI security risks in 2026 are really identity risks