Security validation inside the AI code generator you already use.
Galen© helps AI code generators deliver cleaner code the first time by reviewing generated or modified code, returning proof-backed findings, supporting remediation, and validating the correction before acceptance.
The problem with insecure AI-generated code
AI code generators are extraordinarily productive — and that is exactly what makes insecure output so dangerous. A modern AI assistant can produce hundreds of lines of working code in seconds, but it has no durable understanding of your application's trust boundaries, tenant model, authentication scheme, or sensitive-data handling rules. It generates code that looks correct, compiles, and passes basic tests while quietly omitting the guards that actually protect users and data.
The most common failure modes we see in AI-generated code include:
- Missing authorization checks — endpoints that confirm the user is logged in but never verify the user owns the resource being accessed or modified.
- Broken tenant isolation — queries that filter by ID but not by tenant, organization, or workspace, allowing cross-account data leaks.
- Unsafe input handling — direct interpolation into queries, file paths, shell commands, or template strings without validation or escaping.
- Silent privilege escalation — admin-only routes or mutations exposed without role checks because the generator inferred the wrong scope.
- Sensitive data in responses — full user objects, internal IDs, secrets, or tokens returned to clients that should never see them.
- Weak or missing rate limits, CSRF, and CORS — protections the generator omits because they were not explicit in the prompt.
These issues frequently survive into pull requests, code review, and production. Developers trust working code, reviewers are time-constrained, and AI tools have no security feedback loop — they do not know whether what they just produced is actually safe in the context of your application. The result is a steady increase in security debt that scales with the speed of AI-assisted development.
The Galen© security layer
Detailed examples
A developer asks an AI coding tool to create a customer invoice endpoint. The generator produces a route that authenticates the request and returns the invoice by ID. Galen© identifies that the endpoint checks login but does not verify customer ownership of the invoice, flags this as a tenant-leak risk, and returns a tenant-scoped correction that constrains the query to invoices owned by the authenticated customer. The fix is re-reviewed and validated before the developer accepts the final code.
The generator scaffolds a "delete user" endpoint and a "change plan" endpoint as part of a settings page. Both require authentication but neither checks that the caller has an admin role. Galen© flags both as privilege-escalation risks, points to the exact missing guard, and proposes a role assertion using the project's existing authorization helper rather than introducing a new pattern.
An AI assistant generates a search endpoint that interpolates a user-supplied filter string directly into a database query. Galen© identifies the injection path, explains why the existing input validation is insufficient, and proposes a parameterized query plus a validated filter schema. The corrected version is validated end-to-end before the developer commits.
A profile endpoint returns the full user record, including password hash, internal flags, and API tokens. Galen© detects the over-exposure, identifies the fields that must never reach the client, and returns a corrected response shape that matches the application's existing public user contract.
Designed to operate alongside AI coding tools
Galen© is designed to live where the developer already works. Instead of forcing teams to adopt yet another standalone security tool, Galen© integrates into the AI coding workflow itself — reviewing generated code in the same loop where it is produced, suggested, and accepted. This produces a set of compounding benefits that a downstream scanner or post-merge audit cannot match:
- Zero workflow disruption. Developers keep using the AI tool they already trust. Galen© runs in the same suggest-and-accept loop — there is no separate UI to learn and no context switch.
- Earliest possible feedback. Issues surface the moment code is generated, not days later in a scan report. The developer is still in context and can act immediately.
- Lower remediation cost. Fixing an issue before it lands in the repository is dramatically cheaper than fixing it in code review, CI, staging, or production.
- Cleaner pull requests. Security reviewers see fewer findings, so the ones that remain get the attention they deserve. Review cycles shorten and merge velocity improves.
- A real security feedback loop for the generator. Galen© tells the AI tool not just what is wrong but why, so the next generation in the same session is more likely to be correct.
- Proof-backed findings, not vibes. Every finding includes the evidence — the vulnerable path, the violated assumption, and a validated correction — so developers do not have to argue with false positives.
- Consistent guardrails across tools. Teams that use more than one AI coding assistant get the same security posture across all of them, instead of relying on whichever tool happens to have the best built-in checks that week.
- Scales with AI-assisted development. As the volume of AI-generated code grows, Galen© scales with it — the security review keeps pace with generation, not with human review capacity.
- Aligned with how developers actually ship. Suggest, accept, iterate. Galen© meets that rhythm rather than interrupting it.
Designed to operate inside or alongside AI coding tools such as the following. Integrations are illustrative — contact us for current availability.
Pricing
Service availability note: Some Telhawk services, features, integrations, and delivery models may be in limited availability, private beta, pilot stage, or not yet generally available. Please contact Telhawk to confirm current availability, scope, and delivery options.