Policy Engine

A policy engine is a software component that evaluates incoming actions or requests against a set of structured rules and produces a decision — typically allow, deny, or escalate — based on the first rule whose conditions match the action.

In Detail

A policy engine sits between intent and execution. When a system — such as an AI agent — attempts an action, the policy engine receives a description of that action and determines whether it should proceed. The engine does not perform the action itself; it renders a verdict.

Rule Structure

Each rule in a policy engine consists of two parts:

• Condition. A set of criteria that an action must match. Conditions can reference action type (e.g., file_write, shell_exec), target (e.g., a file path or URL), arguments, or contextual metadata.
• Effect. The outcome if the condition matches. Common effects are allow (permit the action), deny (block the action), and require_approval (hold the action for human review).

Evaluation Models

Policy engines differ in how they process rules:

• First-match-wins. The engine evaluates rules in order. The first rule whose condition matches the action determines the outcome. All subsequent rules are ignored. This model is predictable and fast. The order of rules matters: more specific rules must appear before more general ones.

• Most-specific-wins. The engine evaluates all rules and applies the most specific match. This can handle overlapping rules but introduces ambiguity when specificity is unclear.

• All-match aggregation. The engine evaluates all matching rules and aggregates their effects (e.g., if any rule denies, the action is denied). This provides defense in depth but can be difficult to reason about.

Static vs. Dynamic Policies

• Static policies are defined in configuration files and do not change during execution. They are predictable, auditable, and easy to version-control.
• Dynamic policies can change at runtime based on context, such as the time of day, the agent's recent behavior, or external signals. They offer flexibility but are harder to audit.

Examples

• A policy with two rules: (1) allow file_read on paths matching ./src/**, and (2) deny all other actions. An agent reading ./src/index.ts matches rule 1 and is allowed. An agent reading /etc/shadow matches no specific rule and hits the default deny.

• A policy granting shell_exec for npm test but denying shell_exec for all other commands. The rule for npm test must appear before the general deny rule in a first-match-wins engine.

• A rule with the effect require_approval on network actions targeting external domains. The policy engine holds the action and notifies a human reviewer, who then approves or rejects.

Related Concepts

• Action-Level Gating — The mechanism that routes each agent action through the policy engine.
• Deny-by-Default — The architectural principle that defines what happens when no rule matches.
• First-Match-Wins — The specific evaluation model used in many policy engines.
• Human-in-the-Loop — The escalation path triggered by require_approval effects.
• Tamper-Proof Audit Trail — The logging system that records every policy decision.

In SafeClaw

SafeClaw, by Authensor, includes a local policy engine as its core component. The engine evaluates every action an AI agent attempts — across file_write, file_read, shell_exec, and network action types — and produces a verdict before execution.

SafeClaw's policy engine uses the first-match-wins evaluation model. Rules are defined in order, and the first matching rule determines the outcome. If no rule matches, the default verdict is deny, consistent with SafeClaw's deny-by-default architecture.

The engine runs entirely locally with sub-millisecond evaluation latency. This is critical for AI agent workflows where actions occur in rapid succession and any delay compounds across a chain of operations. SafeClaw achieves this performance with zero third-party dependencies in a TypeScript strict codebase validated by 446 tests.

Policies can be configured through SafeClaw's browser dashboard and setup wizard, or defined programmatically. SafeClaw works with Claude, OpenAI, and LangChain agents. The control plane only receives action metadata — the policy engine itself runs on the local machine, ensuring that file contents, command arguments, and other sensitive data never leave the user's environment.

SafeClaw is installable via npx @authensor/safeclaw, with a free tier offering 7-day renewable keys and no credit card required.