Built around how web applications actually work.
A route can require sign-in and still expose another customer’s records. A database query can be safe from injection and still return fields the caller should never see. Security depends on how endpoints, permissions, business logic, and data connect.
Galen Policy is purpose-built to analyze those relationships in web applications. It follows supported code paths and the controls that govern them, helping uncover missing ownership checks, inconsistent permissions, and unintended data exposure that an isolated edit can hide.
Your security context outlasts the conversation.
AI-only scans depend on what a model sees, retrieves, and infers during each review. As a conversation grows or a new session begins, an earlier security requirement can fall outside its working context. Repeating an instruction does not establish that the resulting code follows it.
Galen Policy retains a structured application map and policy baseline outside the model’s working memory. Later checks analyze the current code against that saved reference. Your next feature can be checked against the same approved intent, even when a different coding assistant writes it.
Some tools also retain graphs, rules, or review history. What matters is how that knowledge is used. Galen derives findings from retained code facts and verifies their derivation, giving AI focused evidence to explain and act on.
Your application’s rules become a standard you can check.
Common vulnerability patterns are only part of security. Your application also has its own rules about who may read, change, export, or expose particular data.
Galen Policy recovers access rules reflected in the code for your team to review, correct, and approve. Existing behavior is the starting point; unsafe access does not become approved simply because it already exists.
Later checks identify departures from that baseline: a new permission, a removed guard, additional sensitive fields in a response, or a new write operation without the expected protection. Intentional changes can receive explicit approval. Unintended changes become concrete findings measured against your recorded decisions.
Look beyond the promise of “secure AI.”
Security products combine these approaches in different ways. Some already retain context, run deterministic analysis, or enforce custom rules. Galen Policy brings application mapping, approved access intent, drift detection, and evidence into one focused workflow for web applications.
| Common approach | What to distinguish | The Galen Policy difference |
|---|---|---|
| Model-led code review | A model interprets the code and context it receives. A convincing explanation still needs evidence that the relevant path and protection were evaluated. | Deterministic analysis derives the finding from retained code facts and checks the derivation. AI helps explain the result and propose corrections. |
| Coding instructions and edit-time guidance | Prompts, rules files, and contextual advice tell an agent what to preserve. Delivering guidance alone does not establish that the code enforces it. | Checks the resulting code against the approved baseline, with evidence of the affected access path and any departure from the rule. |
| General vulnerability scanning | Reusable rules and data-flow analysis find important weaknesses. Application-specific access intent may require separately authored rules or models. | Recovers the application’s access relationships for approval, then checks later changes against that recorded intent alongside supported vulnerability analysis. |
| Graphs and reusable AI context | Retained maps and review memories improve context. Where a model makes the security judgment, that judgment still needs validation. | Uses the map as an analysis foundation. Findings carry the code relationships and checked derivation supporting the conclusion, with explicit evidence gaps. |
| Controls over coding-agent actions | Restrictions on tools, credentials, network access, or commands protect the development environment. They answer a different question from what the resulting application permits. | Examines the web application’s own authorization and data-access behavior: which actor can reach which operation, resource, or fields. |
Compare what establishes the finding, how approved intent is recorded, and how the next change is checked. Those are the foundations Galen Policy keeps together as your application evolves.
See where the rule broke—and why it matters.
Suppose customers may access invoices only within their own tenant. An existing view checks that boundary. A new export route checks sign-in but retrieves invoices by ID without the tenant restriction.
Galen Policy connects the export path, the data it reaches, and the missing protection to the approved rule. The finding gives your developer or coding agent a specific cause to correct.
- Claim
- A customer can reach another customer’s invoice.
- Entry point
- Invoice endpoint → record lookup → response
- Expected boundary
- The invoice belongs to the caller’s tenant.
- Observed control
- Sign-in is checked; tenant ownership is not.
- Correction
- Enforce the tenant boundary at the lookup.
- Recheck
- Evaluate the corrected path and record the outcome.
Illustrative evidence summary, not actual Galen output.
The security conclusion stays anchored to code evidence. Where the analysis is incomplete, Galen makes the gaps explicit. The retained map supports later checks without requiring an AI model to hold the entire repository in one prompt.
Build with your assistant. Check with Galen Policy.
Run the standalone CLI yourself, or have a coding agent with terminal access run it when configured or instructed. Review the finding, correct the implementation, and run the check again.
The application map, approved baseline, and finding evidence remain available across development sessions. Your team can keep building while checking each new change against the security intent it has already approved.
Results depend on supported languages, frameworks, and available evidence. Use Galen Policy alongside tests and code review.