AI policy enforcement
Take AI policy from audit mode to enforcement
Halo ties structured policy bundles to browser controls, model and tool gateways, and selected runtime checks. Teams can inspect decisions in audit mode, adjust rules, and enforce selected outcomes with explanations and exportable records.
The challenge
Policy intent has to reach the work
Rules need to behave predictably across browser, gateway, and runtime paths, with a review step before teams change how activity is handled.
Rules span different surfaces
Browser controls, model requests, tool actions, and runtime checks have different points where a policy decision can apply.
Enforcement can disrupt a workflow
Teams need to inspect match behavior and exceptions before selected rules change what users or agents can do.
Decision context can be scattered
Reviewers need to know which rule matched, which action followed, and where the decision was recorded.
How Halo solves it
Stage policy across supported paths
Start with structured rules, apply them at the relevant control points, and keep records for review as selected policies move to enforcement.
Govern
Policy Engine
Create versioned policy bundles with allow, deny, or alert outcomes in structured schema or YAML. Run selected bundles in audit mode so teams can examine matches before enforcement.
Discover
Halo Browser
Apply audit, warn, or block modes, domain rules, and regex-based DLP checks on supported AI sites. Browser decisions give the policy review a view of activity outside gateway paths.
Secure
Model Gateway
Check model requests and responses through the separate model gateway with configured identity and policy. Its decisions add model traffic to the review before rules are enforced.
Secure
MCP and Tool Gateway
Apply tool-level checks to configured MCP and tool traffic through its separate gateway. This extends selected policy decisions to agent actions that call tools.
Secure
Runtime Guardrails
Use selected PII, secret, prompt injection, moderation, and language checks on supported gateway paths, with masking or redaction where configured. These results give policy owners more context for tuning.
Govern
Audit and Evidence
Keep policy decisions, explanations, approvals, and configuration changes available for review, with audit and gateway exports in CSV or JSON. Teams can use the records to assess the effect of selected enforcement changes.
Who's involved
Keep policy ownership close to the workflow
Policy owners, control operators, builders, and reviewers agree on what each rule should do and where it applies.
Security leaders
Often starts here
Write structured rules, choose the decision outcomes, review matches in audit mode, and approve the move to selected enforcement.
IT and platform teams
Coordinate browser and gateway configuration, define the rollout scope, and review operational changes after enforcement begins.
AI builders
Describe the model and tool paths their workflows use, test representative requests, and report policy matches that need adjustment.
Governance, risk, and compliance
Document the review criteria and evidence needed for the organization’s governance process, then track decisions in its control records.
Suggested pilot
Suggested policy enforcement pilot
This is a suggested plan, and timing depends on your environment and the browser, gateway, and agent paths in scope. Browser and endpoint discovery, when included, is rolled out with guidance from the Halo team.
- Week 1
Choose a policy and its path
Set up the pilot scope with one structured policy, a browser or gateway path, and representative test activity. At the end of the week, teams can show the intended outcome, request types, and review owners.
- Weeks 2 and 3
Observe decisions in audit mode
Set up the selected policy bundle in audit mode and capture its matches and explanations. At the end, teams can show the observed decisions and the cases that need rule changes.
- Week 4
Tune rules and review exceptions
Set up revised structured rules, browser settings, or gateway checks for the agreed scope, then repeat the test activity. At the end, teams can show the revised policy and the review notes for selected cases.
- Week 5
Enforce a selected rule
Set up enforcement for the agreed policy scope and confirm who will monitor its records. At the end, teams can show the configured decision, explanation, and export from the test activity.
Outcomes
What you can show after the pilot
A versioned policy decision
Show the structured rule, selected outcome, and observed matches used during review.
A documented rollout choice
Show which policy scope moved from audit mode to selected enforcement and who reviewed that decision.
Evidence for follow-up
Show a sample of decisions and explanations with an audit or gateway export for the pilot path.
FAQ
Questions, answered
Audit mode records policy matches and explanations without enforcing the outcome. Teams can review representative activity, adjust structured rules, and select which decisions are ready for enforcement.
Halo Browser supports audit, warn, and block modes for supported AI sites, along with domain rules and regex-based DLP checks. Content inspection requires customer-approved expanded permissions and is off in the standard configuration.
Model requests and responses can be checked through the separate model gateway, while MCP and tool calls use the separate MCP and tool gateway. Halo also has an API and dispatch gateway for supported traffic paths.
No. Halo policy authoring uses structured schema or YAML, with versioned bundles and allow, deny, or alert outcomes. Decision explanations help reviewers understand which configured rule produced an outcome.
Selected classifiers for PII, secrets, prompt injection, moderation, and language run on supported gateway paths. Masking or redaction is available on supported paths when configured.
Plan your AI policy enforcement pilot
Bring one real use case to a walkthrough. We will map what you need to see, the decisions you need to make, and the evidence your team will review.