How the SafeClaw Policy Engine Works: A Technical Deep Dive

SafeClaw's policy engine is the core component that decides whether an AI agent's action is permitted or blocked. It evaluates every action against a set of rules and returns a decision in sub-millisecond time. This article explains the internals: how rules are structured, how matching works, how evaluation order determines outcomes, and how the engine achieves the performance it does.

The Role of the Policy Engine

When an AI agent -- whether running on Claude, OpenAI, or LangChain -- attempts an action, SafeClaw intercepts it before execution. The action is described as a structured object with a type (file_write, shell_exec, network) and parameters (file path, command string, URL). The policy engine receives this action description and must return one of two answers: allow or deny.

This decision must happen fast enough that the agent does not notice the overhead. It must happen locally, without network round trips. And it must be deterministic -- the same action against the same ruleset must always produce the same result.

Rule Structure

A SafeClaw policy is an ordered list of rules. Each rule has three components:

1. Matcher: Defines which actions the rule applies to.
2. Conditions: Optional additional constraints that must be satisfied.
3. Effect: The decision to apply if the rule matches -- either "allow" or "deny."

Rule:
  matcher:
    action: "file_write"
    path: "/workspace/**"
  conditions:
    - size_limit: 10485760  # 10 MB
  effect: "allow"

This rule says: allow file_write actions to paths under /workspace/, provided the file size does not exceed 10 MB.

Another example:

Rule:
  matcher:
    action: "shell_exec"
    command: "git *"
  conditions: []
  effect: "allow"

This rule allows shell_exec actions where the command starts with "git" -- permitting git operations while blocking other shell commands.

Action Types

SafeClaw recognizes three primary action categories:

• file_write: Any operation that creates, modifies, or deletes files on the file system. The matcher can constrain by path pattern.
• shell_exec: Any operation that executes a shell command. The matcher can constrain by command pattern.
• network: Any operation that makes an outbound network request. The matcher can constrain by URL pattern, domain, port, or protocol.

Matching Algorithm

When an action arrives for evaluation, the policy engine walks through the rules in order, from first to last. For each rule, it performs the following:

Step 1: Action type check. Does the rule's matcher action type match the incoming action's type? If not, skip this rule.

Step 2: Parameter matching. Do the rule's matcher parameters match the incoming action's parameters? This uses glob-style pattern matching for paths and commands. For example, /workspace/* matches any path under /workspace/, and git matches any command starting with "git".

Step 3: Condition evaluation. If the rule has conditions, are they all satisfied? Conditions are evaluated against the full action context. If any condition fails, the rule does not match.

Step 4: Effect application. If the rule matches (all three checks pass), the rule's effect is returned as the decision. No further rules are evaluated.

This is the first-match-wins model.

First-Match-Wins Evaluation

The evaluation order of rules is critical. The policy engine does not evaluate all rules and then decide -- it stops at the first rule that matches. This has important implications for policy design:

Rule 1: action=shell_exec, command="rm -rf *",    effect=deny
Rule 2: action=shell_exec, command="git *",        effect=allow
Rule 3: action=shell_exec, command="*",            effect=deny

With this ruleset:

• rm -rf / matches Rule 1 and is denied.
• git push does not match Rule 1 (pattern mismatch), matches Rule 2, and is allowed.
• curl https://evil.com does not match Rule 1 or 2, matches Rule 3 (wildcard), and is denied.

This model is borrowed from firewall rule evaluation (iptables, pf, Windows Firewall). It is well-understood, predictable, and fast. Engineers who have configured firewall rules will find SafeClaw's policy model immediately familiar.

The Default Deny Rule

At the end of every SafeClaw ruleset, there is an implicit default deny rule. If no explicit rule matches an action, the action is denied. This is the deny-by-default architecture.

Rule 1: action=file_write, path="/workspace/**",  effect=allow
Rule 2: action=network,    url="https://api.github.com/*", effect=allow
[implicit] Rule N: action=*, effect=deny

In this example, the agent can write files under /workspace/ and make requests to the GitHub API. Every other action -- shell commands, network requests to other domains, file writes outside /workspace/ -- is denied by default.

You never need to write deny rules for things you have not thought of. The default deny catches everything you did not explicitly permit.

Condition Types

Conditions provide additional constraints beyond pattern matching. The policy engine supports several condition types:

• Size limits: Constrain file_write actions by maximum file size.
• Rate limits: Constrain any action type by frequency (e.g., no more than 10 network requests per minute).
• Time windows: Allow or deny actions based on time of day or day of week.
• Content patterns: For file_write and shell_exec, match against the content or command arguments using regex patterns.

Sub-Millisecond Performance

The policy engine evaluates actions in sub-millisecond time. This is achieved through several architectural decisions:

Local evaluation. The engine runs in the same process as the SafeClaw client. There are no network round trips, no RPC calls, no serialization/deserialization overhead for policy evaluation. The action object is already in memory; the rules are already in memory; the evaluation is a series of comparisons and pattern matches.

Compiled patterns. Glob patterns in rule matchers are compiled to regular expressions once, when the policy is loaded. During evaluation, the engine matches against pre-compiled regexes, not raw glob strings. This avoids the cost of glob-to-regex conversion on every evaluation.

Early termination. The first-match-wins model means the engine stops as soon as it finds a matching rule. For well-ordered policies (specific rules first, general rules last), the most common actions match early in the list, minimizing the number of rules evaluated.

No allocations in the hot path. The evaluation path is designed to avoid unnecessary object allocations. The action description, the rule list, and the evaluation result are the only objects involved. No intermediate data structures are created during evaluation.

No dependencies. The engine has no runtime dependencies. There is no framework overhead, no abstraction layers, no dependency injection containers. The evaluation function is a tight loop over an array of rules.

The result: policy evaluation adds negligible overhead to agent operations. An agent performing 1,000 actions per second would spend less than 1 millisecond total on policy evaluation. The security layer is effectively invisible from a performance perspective.

Simulation Mode

SafeClaw includes a simulation mode that evaluates policies without enforcing them. In simulation mode, every action is allowed, but the policy engine still evaluates the action and records what the decision would have been.

This is useful for:

• Policy development. Write rules, run the agent, and see which actions would be allowed or denied without actually blocking anything.
• Policy migration. When changing from one policy to another, run both in simulation and compare the results.
• Audit and analysis. See the full spectrum of actions an agent attempts, including those that would normally be blocked.

Policy Lifecycle

Policies in SafeClaw follow a straightforward lifecycle:

1. Authoring. Rules are defined in the SafeClaw configuration or through the browser dashboard's setup wizard.
2. Loading. When SafeClaw starts (via npx @authensor/safeclaw), the policy is loaded and patterns are compiled.
3. Evaluation. Every intercepted action is evaluated against the loaded policy.
4. Auditing. Every evaluation is recorded in the cryptographic audit trail (SHA-256 hash chain).
5. Updating. Policies can be updated through the dashboard. Updated policies are reloaded and patterns are recompiled.

Designing Effective Policies

The first-match-wins model rewards careful rule ordering. Here are principles for effective policy design:

1. Most specific rules first. A rule denying rm -rf should come before a rule allowing all git commands, which should come before a catch-all deny.
2. Explicit deny before broad allow. If you want to allow most network access but deny requests to specific domains, place the deny rules for those domains before the broad allow rule.
3. Use the default deny. Do not write a catch-all deny rule at the end of your policy -- it is already there. Focus your rules on what you want to allow.
4. Start restrictive, loosen as needed. Begin with a minimal policy that allows only what the agent needs. Use simulation mode to identify blocked actions that should be permitted, and add rules accordingly.

For more on the Authensor framework that powers SafeClaw, visit authensor.com.