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Contains compromised AI capabilities, preserves evidence, scopes derived data and actions, remediates boundaries, and verifies safe recovery.
Tracks trust, versions, integrity, provenance, review, and rollback across models, adapters, prompts, datasets, tools, and parsers.
Establishes server trust, audience-bound authorization, least privilege, capability filtering, approval, and safe handling of untrusted results.
Uses workload identity, separate control and data planes, least privilege, scoped model access, and auditable administration.
Protects knowledge ingestion with source trust, provenance, validation, quarantine, review, versioning, and rollback.
Builds a readable request with stable instructions and clearly delimited, provenance-aware untrusted inputs.
Classifies tools by consequence and applies distinct permissions, validation, confirmation, idempotency, and audit controls.
Maps assets, trust boundaries, untrusted content, model influence, actions, and abuse cases before selecting controls.
Maps risk tiers and policy statements to owned, versioned, testable runtime controls, evidence, exceptions, and review triggers.
Builds a resumable tool workflow that validates model proposals, authorizes every action, requires approval for side effects, and remains idempotent under retries.
Instruments an AI workflow with correlated stage spans, low-cardinality versions, usage and outcome signals, and explicit content-capture controls.
Runs containment and recovery for an agent that ingested malicious instructions from retrieved content and attempted an unauthorized outbound tool call.
Reviews a managed AI proposal against data flow, identity, region, retention, quota, observability, portability, and recovery requirements.
Combines quality, reliability, cost, security, privacy, and recovery evidence into a realistic launch and incident game day.
Designs index records that support filtering, authorization, freshness, citations, diagnosis, and safe reprocessing.
Applies parsing, schema and domain validation, authorization, confirmation, idempotency, limits, and audit before execution.
Places provider guardrails as one configurable defense while preserving application authorization, validation, testing, and incident evidence.
Prefers typed APIs for reliable actions and reserves UI automation for constrained cases with stronger observation, approval, and recovery.
Uses pre-filtering for authorization and hard constraints while recognizing engine-specific recall and performance behavior.
Explains how generated text, markup, code, URLs, and tool arguments can become injection or integrity risks downstream.
Places validation, authorization, limits, state transitions, and approval outside the model while acknowledging that guardrails reduce rather than eliminate risk.