Proof-backed AI security

Turn AI security findings into validated fixes.

For code, APIs, AI agents, and AI-generated software.

AI can generate more vulnerability findings than teams can triage. Using proof-backed evidence, Telhawk's Galen© engine helps prove what is real, prioritize what matters, guide remediation, and validate that corrections worked.

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Find the risk.·Prove it.·Prioritize it.·Help fix it.·Validate the correction.

Claude Mythos

GPT 5.5

Sec-Gemini

Copilot

Microsoft Security Copilot

Grok

LLaMA3

Compatible with all Frontier AI models

The Galen© Proof Pipeline

Inputs

Code · APIs · AI agents · AI-generated software · existing AI findings

Galen© proof engine

Galen© Proof Engine

Maps the evidence behind the risk

Code pathData flowMissing guardPermissionsFix context

AI / LLM layer

AI Security Review

LLMs reason over structured evidence, not disconnected raw inputs

Focused contextEvidence packageRisk reasoning

Proof + Guidance

Proof-backed finding with remediation direction

Validation

Corrected issue reviewed and confirmed

Validated fix

Galen© gives AI security review the structured evidence it needs to move from possible findings to proof-backed outcomes.

The AI security bottleneck

AI findings can cost hundreds of thousands of dollars to prove, fix, and validate.

AI security tools can generate thousands of findings, but generating findings is only the beginning. Every result still requires review, proof, prioritization, remediation, validation, and documentation. Telhawk uses Galen© to transform raw AI findings into proof-backed, prioritized, remediation-ready outcomes with validation and audit-ready evidence—reducing work that traditionally takes weeks or months to just hours.

Finding overload

AI audit tools can generate thousands of findings, including speculative, duplicate, or low-value issues.

Expensive human review

Every result still needs people to decide whether the issue is real, exploitable, and worth fixing.

Developer staffing burden

Companies may need more developers and security engineers just to review, fix, retest, and document AI-generated findings.

Remediation backlog

Large finding volumes can turn into months of engineering work before the business sees validated security improvement.

Validation uncertainty

A suggested fix is not the same as a validated fix. Teams still need to prove the risky path was actually closed.

Audit documentation gaps

Security leaders need durable proof, remediation records, and validation status — not screenshots, chat logs, or raw AI output.

Turn Thousands of AI Findings Into Validated Outcomes in Hours

Hours, not weeks

Without Telhawk

The traditional grind

AI scanner generates thousands of findings
Security teams manually review results
Engineers investigate and prioritize issues
Developers build and test fixes
Teams manually validate remediation
Audit evidence is collected and documented

Weeks or months

With Telhawk + Galen©

Proof-backed, validated outcomes

Findings are automatically correlated and prioritized
Evidence and exploitability are validated
Remediation recommendations are generated
Fixes are verified through automated validation
Audit-ready evidence packages are created
Results are tracked and documented automatically

Completed in hours

AI tools generate findings. Telhawk delivers validated remediation and audit-ready proof.

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The Telhawk difference

Most tools produce alerts. Telhawk produces evidence.

Galen© is designed to provide the proof behind a finding: the affected code path, security-relevant data flow, missing guard or control, remediation context, and validation status after correction.

Vulnerable path identified

Galen© pinpoints the affected route, handler, or agent action.

Proof attached

Code path, data flow, missing guard, and permission boundary are bound to the finding.

Fix guidance provided

A concrete, contextual remediation recommendation accompanies the proof.

Correction validated

A smarter AI still needs better evidence.

An LLM can read code, explain logic, suggest vulnerabilities, and generate remediation ideas. But alone, it may miss relationships across routes, handlers, permissions, data flows, and guard conditions. Galen© gives AI security workflows structured proof so the task becomes focused and verifiable.

Raw LLM prompt

"Review this codebase and find security issues."

Telhawk-enhanced prompt

"Here is the vulnerable route, data path, dangerous input, missing authorization guard, sensitive operation, recommended remediation, and corrected version. Determine whether the vulnerable path remains."

That is a fundamentally different problem.

Commercial offerings

Telhawk runs the audit for you.

Expert-led audits for code, APIs, AI agents, access paths, data flows, remediation, and validation. Best for high-stakes reviews, enterprise requirements, diligence events, and teams that want a completed security outcome.

Explore Managed Audit

Secure portal, repository, API, or workflow access to Galen©-powered audits for developers, AppSec teams, SaaS companies, and enterprises.

Explore Direct Audit

Security review inside AI coding workflows.

Galen© reviews AI-generated or AI-modified code before it reaches the developer, repository, pull request, or production pipeline.

Explore Code Generator Security

MSPs, MSSPs, consultancies, platforms, and resellers can offer proof-backed AI security audits without building their own analysis engine.

Explore Partner Model

Do not spend months sorting through AI security findings.

Talk to an Expert Compare Raw AI vs Telhawk