Resources for proof-backed AI security.
Explore Telhawk's thinking on AI security validation, API authorization, AI agent risk, remediation, and proof-backed security methodology.
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Practical security observations on AI-generated code, vulnerability validation, remediation backlogs, and modern AppSec workflows.
Why raw AI findings need proof, prioritization, remediation context, and validation before teams can act with confidence.
Guidance on tenant boundaries, object ownership, role permissions, sensitive API actions, and cross-customer exposure risk.
Security considerations for AI agents, MCP servers, tools, connectors, RAG workflows, prompt injection, and excessive autonomy.
How Telhawk and Galen© move from finding discovery to proof, remediation guidance, validation, and audit-ready reporting.
Secure code review and source code audit for AI-generated and hand-written codebases, with proof-backed findings and validated remediation.
API security testing and penetration testing covering authorization, tenant isolation, and the full OWASP API Top 10.
AI Security Findings Validation
A content cluster on validating AI-generated security findings — from frontier LLM code review and AI security tools to AI-agent and MCP workflows.
Pillar resource on moving from raw AI-generated findings to proof-backed security outcomes across code, APIs, agents, and AI-written software.
LLMs can generate findings at scale — evidence, context, prioritization, remediation guidance, and validation are what make them usable.
How Mythos-style AI security findings change the market — and why validation, not generation, is the next operational challenge.
Why AI security tools can generate noise and how proof-backed validation helps teams prioritize real risk.
The Telhawk workflow in plain English: find the risk, prove it, prioritize it, help fix it, validate the correction.
What to review in AI-agent workflows and MCP servers — tool access, data boundaries, authorization, and post-change validation.