Topic starter
22/09/2026 3:43 pm
TL;DR: AI security now needs four core practices, discover, detect, govern, and prevent, because shadow AI, autonomous agents, and uncontrolled AI workflows are expanding enterprise attack surface faster than traditional application security can track, according to Ovalix. The control gap is no longer just visibility, it is governance across AI usage, data flows, and agent actions before those systems become operational identities.
NHIMG editorial — based on content published by Ovalix: 4 AI Security Best Practices
By the numbers:
- Gartner predicts that by 2030, more than 40% of enterprises will experience a security or compliance incident linked to unauthorized shadow AI.
- Statista forecasts that the number of active AI agents in enterprises worldwide will grow from 28.6 million in 2025 to more than 2.2 billion by 2030.
- According to IBM, 77% of organizations say AI adoption is already outpacing their current governance capabilities.
Questions worth separating out
Q: How should security teams govern shadow AI without slowing adoption?
A: Start with continuous discovery, then classify tools by data access, system connectivity, and provider trust.
Q: Why do AI coding tools increase governance risk for IAM and NHI teams?
A: AI coding tools increase governance risk because they obscure who created the logic, which identities executed it, and whether the resulting automation has the right access scope.
Q: What are the signs that AI governance is failing in the enterprise?
A: Common warning signs include rapid growth in AI use without matching policy coverage, sensitive files being copied into personal accounts, and a large share of AI apps carrying high or critical risk.
Practitioner guidance
- Build a real AI inventory Track public GenAI tools, homegrown AI apps, coding assistants, AI agents, agent skills, MCP servers, and embedded AI tools in one authoritative inventory.
- Instrument continuous AI detection Monitor prompts, data inputs, access events, and anomalous AI behavior in real time rather than relying on periodic reviews.
- Enforce AI governance at the point of use Require approval workflows for sanctioned tools, then back them with automated policy enforcement for data handling, access scope, and audit logging.
What's in the full article
Ovalix's full blog post covers the operational detail this post intentionally leaves for the source:
- Specific examples of how to inventory public AI tools, homegrown apps, coding assistants, and AI agents across the enterprise
- Operational guidance for monitoring prompts, data inputs, and AI activity without relying on periodic point-in-time reviews
- Implementation detail on policy enforcement for approved AI usage, including governance and audit-ready evidence
- Practical prevention examples for blocking risky actions and remediating policy violations in AI workflows
👉 Read Ovalix's 4 AI security best practices for discover, detect, govern, and prevent →
AI security sprawl: what discover, detect, govern, and prevent mean?
Explore further
22/09/2026 3:43 pm
AI security is now an identity and governance problem, not just an application security problem. Once AI tools, agents, and workflows can act across systems, the control challenge shifts from software approval to ongoing authorisation, auditability, and policy enforcement. That is why AI security programmes need visibility into who or what is using AI, what data it can reach, and what actions it can take. For practitioners, the practical conclusion is that AI governance must sit alongside IAM and NHI governance, not beneath it.
A question worth separating out:
Q: How do organisations decide whether to prioritise prevention or monitoring in AI security?
A: They should do both, but start with the controls that reveal where AI is used and how data moves. Without discovery and monitoring, prevention rules have no reliable context. Once the estate is visible, preventive controls can focus on high-risk tools, workflows, and agent actions rather than every AI interaction.
👉 Read our full editorial: AI security needs discover, detect, govern, and prevent
