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15/08/2026 6:08 pm
TL;DR: AI-related attacks have increased nearly 490 percent year over year, according to Grip Security, but the deeper problem is that most AI governance tools stop at discovery and do not enforce control across SaaS identities, OAuth links, and non-human access. Governance without enforcement is observation, not security.
NHIMG editorial — based on content published by Grip Security: Best AI Governance Tools for Enterprises (2026)
By the numbers:
- AI-related attacks have increased nearly 490 percent year over year.
- 80 percent of AI-related incidents involve sensitive or, tive or regulated data.
Questions worth separating out
Q: How should security teams govern AI features embedded in SaaS applications?
A: Treat embedded AI as a machine identity problem with data access implications.
Q: Why do discovery-only AI governance tools leave material risk behind?
A: Because discovery tells you where AI exists, but not whether it is still appropriately authorised or constrained.
Q: What do security teams get wrong about Shadow AI?
A: They often treat Shadow AI as an approval problem for software, when it is usually also an identity problem.
Practitioner guidance
- Inventory AI access at the identity layer Map every AI-enabled SaaS application to the users, service accounts, tokens, and OAuth grants that can reach it.
- Treat OAuth permissions as governed entitlements Review delegated access continuously, not during annual audits.
- Bring non-human identities into AI governance Extend recertification, offboarding, and ownership assignment to bots, API tokens, and service accounts that interact with AI-enabled SaaS tools.
What's in the full article
Grip Security's full webinar covers the operational detail this post intentionally leaves for the source:
- Side-by-side comparison of AI governance tool categories and where each one stops short on enforcement
- Practical breakdown of SaaS identity and OAuth control points that create AI exposure
- Vendor-specific examples of how teams can monitor and constrain non-human access in live environments
- Implementation guidance for shifting from discovery-led governance to continuous control
👉 Read Grip Security's webinar on the best AI governance tools for enterprises →
AI governance tools and the enforcement gap in SaaS environments?
Explore further
15/08/2026 6:16 pm
The AI governance market is solving visibility faster than it is solving control. That matters because discovery without enforcement creates a false sense of coverage. In SaaS environments, AI risk is shaped by delegated access and non-human identities, so governance that stops at inventory will miss the actual exposure path. Practitioners should treat this as a control-plane problem, not a reporting problem.
A few things that frame the scale:
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, according to The State of Non-Human Identity Security.
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, according to The State of Non-Human Identity Security.
A question worth separating out:
Q: Should AI governance sit with security, IAM or the business?
A: It should be shared, with security setting control requirements, IAM managing identities and permissions, and the business owning approved use cases and risk acceptance. AI governance fails when it is treated as a side project because the controls span multiple teams. Clear ownership, access review and change control are the minimum coordination points.
👉 Read our full editorial: AI governance tools still miss identity enforcement in SaaS environments
