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15/08/2026 6:06 pm
TL;DR: AI security risks have moved from model-only concerns to enterprise governance problems, with Obsidian Security arguing that adversarial inputs, data poisoning, prompt injection, and weak AI identity controls now create blind spots across training pipelines, runtime access, and downstream systems. The practical shift is toward continuous AI security posture management, zero-trust access, and identity-first monitoring rather than relying on static application security.
NHIMG editorial — based on content published by Obsidian Security: The Top AI Security Risks Facing Enterprises in 2025
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: What breaks when training data is poisoned before model deployment?
A: The model learns altered patterns as if they were legitimate, so the compromise becomes part of normal behaviour.
Practitioner guidance
- Implement identity controls for AI agents Inventory every AI agent, assistant, and automated workflow that can authenticate to enterprise systems, then assign each one an owner, scope, and expiry policy.
- Harden training and inference data pipelines Require data provenance checks, dataset versioning, and artifact signing across ingestion, training, validation, and deployment.
- Constrain tool-enabled agent actions Apply least privilege to every AI agent that can call APIs, query databases, or trigger workflows, and segment those permissions from human admin access.
What's in the full article
Obsidian Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Attack-by-attack breakdown of adversarial machine learning, data poisoning, and prompt injection
- Implementation blueprint for AI Security Posture Management across discovery, control, and monitoring
- Practical zero-trust guidance for AI agent access, including MFA, microsegmentation, and least privilege
- Examples of ROI and resilience metrics tied to AI security investment
👉 Read Obsidian Security's analysis of the top AI security risks facing enterprises in 2025 →
AI security risks in 2025: where enterprise controls are failing?
Explore further
15/08/2026 6:15 pm
AI security is now an identity governance problem as much as a model risk problem. The article correctly shows that the most dangerous AI failures are not limited to adversarial inputs. They also arise when agents, APIs, and pipelines operate with weak authentication, overbroad permissions, or unclear ownership. That puts AI systems squarely into NHI governance territory because their runtime access must be managed like any other high-risk non-human identity.
A question worth separating out:
Q: Who is accountable when an AI system makes a harmful decision?
A: Accountability should follow the identity chain that authorized, configured, or triggered the action, including the human owner, the platform team, and any delegated agent or tool account. If the organisation cannot name that chain, the governance model is too weak for regulated AI use.
👉 Read our full editorial: AI security risks in 2025 expose identity and data control gaps
