AI Runtime Security Controls: Map Each Decision to Its Telemetry

AI runtime security controls work when an operator can see each decision and act on it. A "protect confidential data" policy is vague. A runtime control needs an input and enforcement point. It also needs an outcome and event, with a named owner across chatbots and RAG, jobs and copilots, as well as agents. I prefer a blunt test: if the SOC sees an alert at 11:17 p.m., can the analyst identify the caller and route, data class and decision, plus the next action on one screen?

TL;DR

• Map each runtime control to its decision input and enforcement point, with a telemetry event and threshold plus an operational owner.
• Cover non-agent paths such as chatbots and RAG requests, plus batch jobs and copilots alongside agent model calls.
• Keep HTTP model-call policy separate from sandboxing and tool authorization, as well as credential security and local execution.
• Record permit and redact outcomes, along with deny and throttle outcomes plus escalation outcomes with identity and policy-version context.

AI runtime security controls need observable decisions

The NIST AI Risk Management Framework calls for production monitoring under MEASURE 2.4. Its post-deployment guidance adds incident response and recovery, plus change management and appeal, with override. The AI RMF source describes outcomes that each enterprise must translate into telemetry.

Use a policy question and runtime decision for every control. Add an event and alert condition, with a named owner. Put them beside the route inventory in the AI security policy template. One screen should expose a denied request and the policy version that denied it. Dashboards without that link create uncertainty.

Identity and authorization controls

Ask if this user or service, including an agent, may invoke the route for this tenant and purpose. Decide permit or deny before forwarding. Telemetry should carry principal and workload identity, role and tenant, route and destination, plus policy version and outcome. Include the correlation identifier.

Alert on missing identity and disabled principals, cross-tenant attempts and unusual route access, plus sudden use of shared service identities. IAM owns identity quality and account state. The AI platform team owns propagation to the model call. Security owns the policy and review logic.

NIST SP 800-53 AC-3 requires enforcement of approved authorizations for users and processes acting on their behalf. The SP 800-53 control catalog supports service-level enforcement, which makes it directly useful for HTTP model routes.

Prompt data and destination controls

Ask if this data class may reach the model and provider in the requested region on this route. Decide whether to permit or deny. When partial handling is allowed, redact or tokenize. Telemetry should include classifier labels and rule identifiers, requested and actual destinations, fallback use and transformation, plus outcome. Store sensitive payloads only where policy permits.

Alert on credentials and regulated identifiers sent to unapproved destinations. Also alert on classification failure and fallback changes. Data owners define classification terms and examples. Privacy and compliance define handling. The AI platform team owns route metadata. Security owns enforcement logic and review criteria.

OWASP LLM02:2025 describes PII and financial details, health records and confidential business data, plus credentials and legal documents as sensitive information at risk in LLM applications. Its mitigation guidance includes access controls and restricted data sources. It also includes sanitization and tokenization, plus redaction.

Prompt-injection and provenance controls

Ask if untrusted content tries to alter model instructions or expand the authorized purpose. Inspect user input and assembled context. For RAG, include document identifiers, source trust, retrieval authorization, and injection signals. Preserve conversation and turn identifiers for chatbots, and input collection plus job identity for batch work.

Telemetry should state signal and content source, plus score or rule and decision. Include removed spans. Alert on repeated attempts and poisoned retrieval sources, along with output deviations. Content owners handle source trust, while the application owns retrieval ACLs. Security owns model-call policy. AI security for RAG systems shows how the layers compose.

NIST AI 600-1 identifies prompt injection as an expanded attack surface and distinguishes direct input from instructions hidden in retrieved data. The Generative AI Profile provides context for runtime detection and response.

Response and downstream-consumer controls

Ask if the response is safe for this identity and its next consumer. Prose carries different risk than output passed to HTML, SQL, a ticketing API, or a workflow. Decide permit, redact, quarantine, or deny.

Telemetry should capture response classification and destination component, rendering mode and matched rule, plus outcome. Alert on sensitive-data disclosure, unexpected structured output, encoded payloads, and policy-denied streams. The consuming application owns safe parsing and output encoding. The model-call enforcement layer can inspect the HTTP response and apply content policy. Downstream commands require authorization at their own execution point.

OWASP LLM05:2025 defines improper output handling as insufficient handling of model output, including validation and sanitization, before it reaches downstream systems. That boundary matters because response inspection and command authorization solve different problems.

Consumption and availability controls

Set the inference this identity, tenant, route, or job may consume during an interval. Decide permit, throttle, queue, or deny. Telemetry should include request count and tokens, concurrency and retries, queue depth and latency, estimated cost and quota, plus outcome.

Alert on burst changes, repeated oversized prompts, retry storms, and quota overrides. Product owners set customer limits. FinOps sets cost thresholds for each route. Platform operations owns capacity. Security investigates abusive patterns.

OWASP LLM10:2025 recommends rate limits and user quotas, timeouts and throttling, plus resource monitoring and queued-action limits in its unbounded consumption guidance. Provider billing totals arrive too late for prevention; route-level telemetry supports the inline decision.

Audit integrity and review controls

The policy question asks which runtime events must support investigation and regulatory evidence. NIST SP 800-53 AU-2 covers event selection and review. AU-3 calls for the event type and time, location and source, plus outcome and associated identity. Add route and model, data classification and policy version, plus correlation identifiers for AI traffic.

Record permits as well as blocks so investigators can reconstruct the sequence before an incident. Alert on missing records and signature failure, ingestion lag and policy-version drift, plus sequence gaps. Security operations owns review, while platform engineering owns delivery. Records management owns retention. AI security incident response should name the query and escalation path.

Controls outside the HTTP model boundary

Agent sandboxes, tool authorization, filesystem permissions, and process isolation belong to their executing systems. Secret storage and non-model outbound connections also belong to their executing systems. DeepInspect owns none of those controls. Correlate their telemetry with model-call records through a run or trace identifier.

For an agent, join the permitted model call to the later tool decision. For a non-agent batch job, join the model request and stored output back to the source dataset. This separation prevents a proxy from claiming visibility into a shell command it never saw and gives incident responders a defensible chain across control owners.

DeepInspect

DeepInspect enforces policy on HTTP traffic between authenticated users or workloads and LLM endpoints. It evaluates identity, route, destination, and content on requests and responses, then writes per-decision records. Applications remain responsible for identity input and retrieval authorization, tools and sandboxes, plus local execution.

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