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21/08/2026 5:01 pm
TL;DR: AI security protects models, data, prompts, and decision pipelines across training, deployment, and runtime, according to AccuKnox, because traditional cloud controls miss AI-specific attack surfaces such as prompt injection, model poisoning, and agent tool abuse. The governance shift is from infrastructure-only protection to enforceable lifecycle controls that bind policy to production behaviour.
NHIMG editorial — based on content published by AccuKnox: What Is AI Security? Securing AI Models Across Training, Deployment, and Runtime
By the numbers:
- Only 44% of organisations are currently using a dedicated secrets management system.
- 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.
Questions worth separating out
Q: How should security teams govern AI models that can call tools and access data?
A: Security teams should govern AI models as non-human identities with named owners, limited scope, short-lived credentials, and continuous authorization.
Q: Why do AI agents complicate existing IAM and PAM controls?
A: AI agents complicate IAM and PAM because they often inherit delegated credentials, operate across multiple systems, and keep acting after the initial approval moment has passed.
Q: What breaks when training data or model artifacts are not integrity-protected?
A: Poisoned data, compromised checkpoints, or untrusted registries can embed behaviour that survives into production.
Practitioner guidance
- Implement lifecycle-scoped AI asset inventory Track models, registries, inference endpoints, vector databases, and agent tools as governed assets so security teams can see where the AI control surface actually exists.
- Bind IAM to AI pipelines and registries Restrict who can train, publish, replace, or pull models by using least-privilege access on datasets, checkpoints, and registry paths, with cryptographic integrity checks for artifacts.
- Enforce runtime allow and deny policies Apply policy controls to prompts, retrieved context, and tool calls so high-impact actions such as exports, writes, and identity changes require explicit enforcement.
What's in the full article
AccuKnox's full article covers the operational detail this post intentionally leaves for the source:
- Layer-by-layer breakdown of training, deployment, and runtime control points for AI systems.
- Examples of how AI-SPM and Zero Trust CNAPP concepts map to AI assets in production.
- Operational guidance on prompts, tool calls, and runtime enforcement for live systems.
- Discussion of how governance evidence can be tied to incident response and audit workflows.
👉 Read AccuKnox's explainer on securing AI models across training, deployment, and runtime →
AI model security across the lifecycle: what changes for teams?
Explore further
21/08/2026 5:16 pm
AI security has become a lifecycle governance problem, not an infrastructure tuning problem. The article is correct to separate training, deployment, and runtime because each phase has different failure modes and different evidence requirements. That maps closely to how NIST CSF and NIST AI RMF treat control ownership: inventory, protection, monitoring, and accountability must bind to the stage where risk emerges. For practitioners, the implication is that AI governance must be evidence-driven across the full lifecycle, not documented once and forgotten.
A question worth separating out:
Q: Who is accountable when an AI agent takes an unsafe action?
A: Accountability should sit with the business owner of the agent, the team that provisioned the access, and the control owners responsible for monitoring and revocation. If no one can answer who approved the identity, the scope, and the oversight model, the governance framework is not complete enough for production.
👉 Read our full editorial: AI security must span training, deployment, and runtime controls
