Topic starter
02/02/2026 9:20 pm
TL;DR: AI workloads running on premises inherit container, Kubernetes, and supply chain risks while adding prompt injection, data leakage, and GPU abuse paths, according to Aqua Security. The security gap is not the model alone but the identity, policy, and runtime controls around it, especially where AI components touch sensitive data and business transactions.
Editorial analysis by NHI Mgmt Group, based on content published by Aqua Security: “Secure AI Infrastructure On-Premises from Day One”.
Key questions
Q: How should security teams govern in-house AI inference workloads?
A: Security teams should govern in-house AI inference workloads as non-human identities with scoped permissions, named ownership, and lifecycle controls.
Q: Why do prompt injection attacks create governance risk for AI agents?
A: Prompt injection creates governance risk because the model often sits in the control path between text input and tool execution.
Q: What signals show that AI runtime controls are failing?
A: Warning signs include unexplained tool usage, access to data outside the expected workflow, repeated policy overrides, and behavioural drift across sessions.
Practitioner guidance
- Map the full AI transaction path Document the client, gateway, context service, inference layer, and compute resources that participate in each AI request.
- Restrict context attachment to least necessary data Limit what internal account data, business rules, or records can be attached to an AI request, and review those scopes as tightly as other privileged application permissions.
- Inspect prompts and responses as governed data flows Capture prompt and response content for security review, strip sensitive material where possible, and alert on jailbreak language, policy bypass attempts, or unexpected disclosure patterns.
Bottom line: On-premises AI security is really a governance problem across identity, context, runtime, and compute boundaries, not just a model-protection problem.
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This topic was modified 1 day ago by NHI Mgmt Group
11/10/2026 3:43 am
Identity and runtime controls are now inseparable in on-premises AI. The article shows that the model is not the security boundary. The boundary is the chain of client, context, inference, and compute services that turn a prompt into business action. That means IAM teams must treat AI requests as governed transactions, not as isolated model calls. Practitioners should design for control across the full runtime path.
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.
A question worth separating out:
Q: How do on-premises AI controls differ from ordinary application security controls?
A: On-premises AI controls differ because they must govern a system that both processes data and can influence actions in real time. Traditional app controls focus on access and code, while AI controls must also bound prompts, context, responses, and compute use. That makes runtime governance essential.
👉 Read our full editorial: On-premises AI infrastructure needs identity and runtime guardrails