The AlgorithmAI capability is moving faster than security, policy, and operational control. Teams cannot answer which data a system can access, which actions it may take, or how unsafe behavior will be detected.
Agents touch sensitive data, call tools, influence decisions, cross trust boundaries, or must pass security and risk review.
CIO, CISO, data, risk, and product leaders accountable for governed AI adoption.
AI systems expand the attack surface through natural-language inputs, retrieved data, external tools, and probabilistic outputs. Governance in policy documents alone cannot constrain runtime behavior.
The security problem begins when an AI system can retrieve sensitive context, generate decisions, or operate tools across trust boundaries. Governance becomes useful only when policy is translated into runtime behavior, testable release criteria, incident response, and evidence an accountable owner can inspect.
Carry user, workload, tenant, and purpose identity through retrieval and action. A model recommendation never expands the permissions of the principal on whose behalf it operates.
Separate system instructions from untrusted content, minimize retrieved data, validate outputs, and enforce tool policy in code. Detection complements these controls; it cannot substitute for them.
Test task quality alongside injection resistance, disclosure, excessive agency, bias risks relevant to the workflow, abstention, and safe failure. Bind results to the exact model, prompt, data, policy, and tool versions.
Provide traceable approvals, risk acceptance, exception expiry, incident runbooks, kill paths, rollback, and reporting suited to the people accountable for the system.
Retrieve permission-filtered documents and answer with source context.
Human accountability. Content owners govern sources; users remain responsible for consequential use.
Engineering constraints. Entitlement leakage, malicious documents, stale sources, retention, and citations.
Investigate a request and propose or execute bounded changes.
Human accountability. Service owners approve material actions and own restoration.
Engineering constraints. Least privilege, action validation, idempotency, blast radius, and emergency stop.
Prepare evidence and a recommendation for an established business decision.
Human accountability. The designated human or rules engine owns the decision.
Engineering constraints. Explanation, data quality, prohibited factors, appeal, reproducibility, and drift.
Map models, prompts, retrieval stores, tools, data classes, users, vendors, and decisions across production and unofficial use.
Separate assistance from recommendations and autonomous action, then assign approval, evaluation, and evidence requirements.
Specify identity propagation, tenant isolation, residency, retention, provider exposure, and prohibited data flows.
Test prompt injection, data exfiltration, unsafe tool use, authorization mismatch, model failure, and bypass attempts.
Gate prompt, model, retrieval, and tool changes while monitoring policy decisions, drift, spend, and overrides.
Connect policies to operating controls, owners, exceptions, evaluations, incidents, and versioned approvals.
Signal. A document, webpage, or tool response attempts to redirect the system.
Architecture response. Isolate untrusted content, restrict tools, validate proposed actions, and evaluate known and workflow-specific attacks.
Signal. Responses or traces expose information outside user, tenant, or purpose boundaries.
Architecture response. Filter retrieval before inference, minimize context and telemetry, apply output controls, and test cross-tenant cases.
Signal. When a control or provider fails, the workflow silently chooses a less governed path.
Architecture response. Define fail-closed behavior for material actions and make degraded modes explicit, observable, and reversible.
Signal. A model, connector, role, or dataset change invalidates the approved control assumptions.
Architecture response. Continuously inventory versions and dependencies, rerun proportional evaluations, and expire approvals when boundaries change.
No. Policy defines intent and accountability. Runtime identity, authorization, data controls, evaluation gates, monitoring, human approval, and incident procedures make that intent operable.
Start from the actual workflow and tool permissions. Test direct and indirect instructions, encoded content, conflicting sources, data-exfiltration attempts, tool manipulation, and multi-step attacks. A pass requires both safe behavior and evidence that external controls stopped prohibited actions.
Enough to reconstruct the material event: principal and purpose, system and model versions, relevant source identifiers, policy decisions, tool requests and results, approvals, outcome, and exception state. Avoid copying sensitive prompt content when identifiers or protected references suffice.
The architecture must reflect the specific decision, data, jurisdiction, product role, and accountable authority. Regulatory labels alone do not define the control; counsel, risk, security, clinical, or other domain owners establish applicability.
How public outcome claims are reviewed before publication.
Explore this resource →Production architecture surrounding governed AI.
Explore this resource →Control operation and evidence inside the platform.
Explore this resource →Background on a voluntary AI risk framework.
Explore this resource →An engineer will assess the technical fit and the next useful decision.