Topic starter
10/06/2026 10:55 pm
TL;DR: AI governance tools point practitioners toward a market built around visibility, policy enforcement, and compliance tracking, according to Netwrix, but the article’s real signal is that tool selection is now inseparable from shadow AI, access governance, and data control decisions. The issue is no longer whether AI activity exists, but whether identity and governance programmes can see and govern it.
Editorial analysis by NHI Mgmt Group, based on content published by Netwrix: “Best AI governance tools and platforms in 2026”.
Key questions
Q: What breaks when shadow AI is not part of the asset inventory?
A: When shadow AI is absent from inventory, security teams cannot apply policy, logging, access review, or remediation to the workload.
Q: Why do organisations need AI security if they already have DLP?
A: Traditional DLP was built for file, email and endpoint transfer patterns, not conversational AI or autonomous AI workflows.
Q: How should teams govern shadow AI in existing IAM programmes?
A: They should treat shadow AI as an identity and access issue, not a standalone AI issue.
Practitioner guidance
- Map AI discovery to ownership and access paths Identify where AI tools, copilots, and embedded features are used, then record the business owner, technical owner, and identities that can invoke them.
- Align AI governance with identity reviews Fold approved AI services, unmanaged AI use, and AI-connected accounts into the same governance cycle that covers entitlements, recertification, and offboarding.
- Treat DLP as a support control Use data controls to reduce exposure, but do not assume DLP resolves unsanctioned AI usage on its own.
Bottom line: Shadow AI creates a governance gap when AI use exists outside formal ownership, inventory, and access review.
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This topic was modified 6 days ago by NHI Mgmt Group
12/06/2026 4:39 am
Shadow AI is a governance discovery problem before it is a tooling problem. If an organisation cannot identify where AI is running, who owns it, and what it can access, then policy enforcement is already late. The strongest signal in this category is not feature breadth but whether the governance model can surface unapproved AI activity across users, teams, and systems. Practitioners should judge the category by discovery coverage first and dashboard quality second.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should security teams prioritise shadow AI discovery or policy writing first?
A: Discovery comes first, because policy is only enforceable when teams know what exists, who owns it, and what it can reach. In practice, policy writing without discovery produces clean documents and weak control. The first useful step is to expose where AI is already embedded, then assign governance to the systems that matter.
👉 Read our full editorial: Best AI governance tools expose a shadow AI governance gap
This post was modified 6 days ago by NHI Mgmt Group