Topic starter
09/07/2026 1:40 pm
TL;DR: 81% of organisations running AI packages have at least one known vulnerability, 29.5% store an AI credential insecurely, and 87% to 98% of AI workloads lack customer-managed encryption across major clouds, according to Orca Security’s 2026 State of AI Security Report. The security gap is now operational, not theoretical, and it demands identity discipline as much as patching.
Editorial analysis by NHI Mgmt Group, based on content published by Orca Security: “2026 State of AI Security: AI Is in Production. Security Isn’t.”.
Key questions
Q: What breaks when AI/ML credentials are exposed in production?
A: The failure is immediate authenticated access.
Q: Why do AI security programs need both data controls and identity controls?
A: Because data controls show where sensitive information lives, while identity controls determine who or what can reach it.
Q: Where do AI deployments most often fail in practice?
A: They fail when teams treat AI as isolated innovation instead of production infrastructure.
Practitioner guidance
- Prioritise exploit-aware patching for AI packages Use exploit availability and deployment reach to rank AI package fixes, then remove internet-facing exposure first where those packages run in production.
- Move AI credentials into governed secret storage Locate API keys, service tokens, and provider credentials used by AI applications and replace any insecure storage location with managed secrets and short-lived credentials.
- Inventory every production AI agent and permission set Document which cloud permissions each agent holds, what data it can reach through retrieval pipelines, and who owns revocation authority for that access.
Bottom line: AI security in 2026 is dominated by exposure conditions that let attackers turn credentials, packages, and cloud defaults into real access paths.
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09/07/2026 1:44 pm
AI security is now an identity governance problem as much as a cloud security problem. The report shows that AI credentials, agents, and model integrations are operating as live access paths into enterprise data and cloud capacity. That means IAM and NHI governance can no longer stop at human users or classic workloads. Practitioners should treat AI identities as first-class governed assets, not application implementation detail.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- 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: How should security teams govern agentic AI as it moves into production?
A: Security teams should govern agentic AI as a class of non-human identity, not as a generic application feature. That means assigning ownership, scoping permissions tightly, logging every tool action, and revoking access on a defined lifecycle. Production rollout should require clear approval points for high-risk actions and continuous monitoring for drift.
👉 Read our full editorial: 2026 AI security risk is now an identity and exposure problem