The instruction requests an operation unrelated to the user's support task.
02 / CONTROL · Runtime AI security and response
Stop unsafe AI interactions before they become actions.
Inspect, authorize, control, and explain AI interactions and autonomous actions at the runtime decision boundary.
AI Firewall
Illustrative product view
INTERACTION / SUPPORT-AGENT-PROD
Decision before tool execution
- 01UserSummarize open cases
- 02RetrievalHidden export instruction
- 03ModelInstruction propagated
- 04AgentSupport purpose
- 05MCP toolBulk CRM export
The delegated service role exceeds the approved purpose for this session.
Require approval or continue with a case-limited read action.
RUNTIME POLICY ENFORCEMENT
Stop unsafe AI interactions and autonomous actions before they create business impact.
A content-only filter cannot determine whether an agent is authorized to use a tool, whether retrieved content changed its intent, or whether a sequence of individually acceptable steps now exceeds the approved purpose.
Manipulated context
Prompts, files, webpages, retrieved content, and tool output can carry instructions that redirect model or agent behavior.
Sensitive-data movement
Personal data, secrets, source code, and proprietary content can cross an unintended model, user, log, or tool boundary.
Excessive agency
An agent can take a valid tool action with the wrong identity, destination, scope, purpose, or accumulated sequence of effects.
CONTEXT-AWARE ENFORCEMENT
Content is one signal. Authority and consequence decide the action.
The same words can be safe or unsafe depending on who is acting, which agent is delegated, what the tool can do, where data will move, and what happened earlier in the interaction.
AI FIREWALLDecision boundaryPolicy before execution
Interaction
Prompts, responses, retrieval, memory, files, tool requests, results, and prior steps
Authority
User, agent, service identity, delegation, permission, approved purpose, and required approval
Reach
MCP server, tool, arguments, destination, data class, model, application, and environment
Risk history
Policy, posture, test evidence, observed behavior, exceptions, and earlier session decisions
OUTCOMES
A runtime control that preserves legitimate work, not a blanket content filter.
AI Firewall makes a proportionate decision and retains the context required to operate, investigate, and improve it.
Allow or transform
Permit the interaction, redact sensitive content, constrain arguments, or route to an approved model, tool, or destination.
Hold or require approval
Pause a consequential action and preserve its full context for a named reviewer and accountable decision.
Block or contain
Stop the request, quarantine the connection, terminate an authorized runtime, or initiate an available response integration.
Explain and preserve
Record what was evaluated, which policy applied, why the decision occurred, what action followed, and what remained permitted.
HOW AI FIREWALL WORKS
Inspect. Decide. Enforce. Respond. Investigate.
Each step remains connected to the interaction, identity, policy, action, and evidence that came before it.
- 01Inspect
See the connected interaction
Review prompts, responses, retrieved content, memory, files, tool calls, results, and agent-to-agent messages.
- 02Decide
Combine content with authority
Use intent, identity, permission, data sensitivity, posture, policy, and behavioral context in the decision.
- 03Enforce
Act before impact
Allow, block, redact, restrict, reroute, pause for approval, quarantine, or terminate at the controlled boundary.
- 04Respond
Contain the unsafe path
Alert operators, revoke credentials, stop agents or tools, isolate connections, and hand off evidence to security operations.
- 05Investigate
Reconstruct the action chain
Trace and replay the user–model–agent–tool–data sequence with its policy and decision history intact.
ENFORCEMENT RECORD
Explain what was evaluated and why the outcome changed.
A block without context creates another investigation problem. A useful decision record connects the interaction to authority, policy, enforcement, and the safer path that remains.
- 01
Observed interaction
The content, retrieval, memory, file, tool request, result, or multi-step behavior evaluated at the control point.
- 02
Identity and authority
The user, agent, delegated identity, approved purpose, applicable permission, destination, and data boundary.
- 03
Policy and reason
The policy owner and version, matched condition, supporting evidence, confidence or uncertainty, and decision rationale.
- 04
Action and continuation
The enforced action, downstream response, permitted alternative, reviewer state, and investigation-ready evidence.
PRODUCT CAPABILITIES
Six capabilities working as one AI Firewall product.
Runtime inspection, guardrails, tool control, identity, data protection, and forensics form one operating experience.
Runtime Inspection
Bidirectional analysis of prompts, responses, context, memory, files, tool calls, and multi-step activity.
Guardrail Studio
Policy authoring, evaluation, staged rollout, exception handling, and change review for application-specific controls.
MCP & Tool Control
Trust, scope, argument, destination, and result controls for tool and MCP interactions.
Agent Identity
Identity-aware authorization for delegated authority, approved purpose, least privilege, and high-impact actions.
RAG & Data Protection
Controls for untrusted retrieval, sensitive inputs and outputs, data handling, and approved destinations.
Response & Forensics
Containment actions, connected traces, decision evidence, investigation, and controlled replay.
TECHNICAL DECISION SYSTEM
From observable interaction to proportionate action.
The Firewall is the runtime decision boundary. It must show where it is inserted, which context reaches the policy, what action is enforceable, and how safe operation continues.
PRODUCT ROLERuntime policy, authorization, enforcement, response, and investigation for AI interactions and autonomous actions.
Available product · deployment scopedWHERE THIS PRODUCT CHANGES THE DECISION
Move from product mechanism to the operating outcome.
A product may lead one solution and provide context or evidence in another. Its role is stated explicitly on each path.01 / GOVERNContext
Govern AI Adoption
Govern AI adoption with current evidence, accountable ownership, and risk-based approval.
Explore the solution →02 / CONTROLLead
Secure Agentic Operations
Authorize every consequential agent action with identity, purpose, context, and policy.
Explore the solution →03 / ASSUREEvidence
Assure AI Releases
Release AI systems with version-bound attack, control, and artifact evidence.
Explore the solution →DEPLOYMENT SCOPE
Confirm enforcement coverage for the selected control point.
Observation, supported actions, failure behavior, and operating ownership are confirmed before implementation.
Control requires an insertion point
Inline enforcement is available only where the proposed application, gateway, API, SDK, endpoint, or agent runtime provides a supported and tested control boundary.
Observable context defines coverage
The Firewall can evaluate only the interaction, identity, tool, data, and history exposed through the selected integration and permitted for processing.
Runtime is not a latency promise
Performance, streaming behavior, timeouts, capacity, regional routing, and safe failure behavior must be measured for the proposed deployment.
Response authority must exist
Credential revocation, quarantine, termination, and other containment actions require an integrated system and explicitly granted authority.
Detection is not infallible
Evaluation must examine false positives, false negatives, bypass paths, policy conflicts, model failure, and operator escalation, not only successful blocks.
Evidence is not certification
A decision trace or framework mapping can support governance and audit work; it does not by itself establish legal compliance or certification.
TECHNICAL FAQ
Deployment, enforcement, and operating fit.
Is this only a prompt filter?
No. AI Firewall covers the interaction path: prompts, responses, retrieved content, memory, identities, tool requests, results, and the action the system is about to take. Observable context is confirmed for the selected integration.
How does it protect agents?
Agent decisions add identity, delegated authority, approved purpose, destination, tool, session, behavior, and multi-step context so the policy can judge the attempted action, not only its text.
Can enforcement be introduced gradually?
The implementation model supports a deliberate progression from visibility and policy evaluation to selected enforcement. Exact observation, failure, and rollout behavior is agreed during technical review.
Which deployment patterns are supported?
AI Firewall supports API, gateway, proxy, SDK, browser, endpoint, and agent-runtime control points. Provider, framework, hosting, interface, data-handling, and residency requirements are confirmed for each deployment.
What makes a decision explainable?
The operator should see what was evaluated, which identity and policy applied, why the decision was made, what action followed, and which safer path remains permitted.
START WITH ONE CONTROL BOUNDARY