LLM security controls for enterprise deployments

    What to have in place before an AI feature reaches customers or internal production data — grouped so each item has an owner.

    By Piotr Zenon Żabrowski · Published · Updated

    Data handling

    • Documented legal basis and processor terms for every inference provider.
    • PII minimisation or redaction before the request leaves your boundary.
    • No training on your data unless explicitly contracted.
    • Prompt and response retention bounded and documented.
    • Data residency confirmed for the actual inference region, not just the provider's default marketing region.

    Architecture

    • Inference calls proxied through a gateway you control; provider keys never in the client.
    • Retrieval scoped per tenant and per user clearance at query time.
    • Tool authority delegated from the user; write actions confirmed.
    • Model output treated as untrusted input everywhere downstream.
    • Runtime prompt and response screening (provider-native or third-party guardrail service) sits in front of the model, not instead of the controls above — it catches known patterns, it does not replace least-authority design.

    Operations

    • Rate limits and per-tenant token budgets with alerting on anomalies.
    • Audit log of prompts, retrieved source IDs, tool calls and outcomes.
    • Fallback behaviour defined for provider outage or refusal.
    • Model or version changes go through change control with regression tests.
    • Red-teaming coverage tracked against a recognised adversarial technique catalogue (MITRE ATLAS) rather than an internal, ad hoc list of things someone thought to try.

    Agent and automation governance

    Once the system plans, chains tools or coordinates multiple agents

    • Each agent's objective is fixed and re-validated before every consequential action; a plan that drifts from the stated goal is a signal, not background noise.
    • Agent-to-agent messages are authenticated and schema-validated — an agent should not accept instructions from a peer it cannot verify.
    • A single agent failure or timeout cannot cascade: circuit breakers and step budgets stop a chain rather than letting it retry indefinitely or fan out into more agents.
    • Any agent capable of code execution or file writes runs in an isolated, disposable environment with no path to production credentials.
    • There is a kill switch that revokes an agent's tokens and tool access without needing to redeploy anything.

    1Sources

    • Base models
    • Fine-tunes
    • Third-party plugins
    • Datasets

    2Gateway

    • Server-side keys
    • Quotas
    • Content filtering
    • Logging

    3Application

    • Retrieval store
    • Tool layer
    • Session identity

    4Assurance

    • Evaluation suite
    • Change control
    • Incident path
    Enterprise GenAI supply chain and its control points

    Frequently asked questions

    Self-host or use an API?

    Self-hosting moves the risk rather than removing it: you gain data control and inherit patching, isolation and capacity work.

    How does this map to OWASP's risk lists?

    Loosely. The OWASP Top 10 for LLM Applications (LLM01–LLM10) covers the model and application layer — injection, output handling, supply chain, unbounded consumption. Once the system hands a model tools and lets it act on its own plan, the OWASP Top 10 for Agentic Applications adds the failure modes that only show up with autonomy: goal hijacking, tool misuse, cascading failures across chained agents, and rogue or compromised agents. Both lists map onto the same three groups below; agentic risk mainly deepens the architecture section.