Ridge Security today announced the availability of RidgeGen™, an enterprise-grade native agentic AI platform for continuous offensive security testing. Unlike traditional automated security testing tools that primarily follow pre-defined catalogs, RidgeGen takes a fundamentally different approach. It’s a multi-agent AI system that fully leverages the reasoning capabilities of modern foundation models. The agents continuously reason, test, validate, and recommend fixes in iterative AI loops. They’re augmented by our proprietary security knowledge base, grounded through specialized toolchains, and protected by a strict three-zone security guardrail architecture to ensure every action stays within the user’s intended scope.
The launch comes as artificial intelligence reshapes both software development and cyberattacks. As AI accelerates code generation and enables attackers to discover and weaponize vulnerabilities faster than ever before, traditional vulnerability scanners and periodic penetration tests are struggling to keep pace. Even existing automated penetration testing tools often fall short, providing limited findings that can leave organizations with a false sense of security. Security teams are increasingly overwhelmed with alerts but lack the evidence needed to determine which vulnerabilities represent real business risks and how to implement rapid remediation.
RidgeGen addresses this challenge by augmenting AI models with Ridge Security’s proprietary security knowledge base, specialized tooling, and enterprise-grade agentic AI framework. It combines AI reasoning with deterministic controls that keep security testing safe, auditable, and explainable. By separating AI reasoning from authority, enforcement, and verification, RidgeGen enables organizations to safely deploy autonomous offensive security while maintaining strict governance over targets, credentials, and testing policies.
“AI has fundamentally changed offensive security,” said Lydia Zhang, President of Ridge Security. “In today’s post-Mythos world, defenders must adopt an offensive mindset. Security teams need to operate with the same level of intelligence and at the same speed as their attackers. RidgeGen delivers what enterprises need: safety, efficiency, explainability, organizational knowledge, and adaptation to each customer’s unique environment.”
According to Jason English, Director and Principal Analyst at Intellyx, organizations face a growing gap between AI adoption and their ability to secure it.
English says, “Whether driven by top-line improvement goals for better customer experience and agility, or bottom-line efficiency and productivity metrics, agentic AI is a promising value lever companies are pulling today. Continuous agentic security testing doesn’t just put a ‘human in the loop.’ Expert offensive agents can give security teams more context, less noise, and better visibility of the most significant risks, so everyone can breathe a little easier with each push to production.”
Unlike traditional security testing tools that identify potential issues based on signatures or known CVEs, RidgeGen autonomously investigates how attackers could chain together vulnerabilities, business logic flaws and misconfigurations to compromise an environment. Every finding is validated with reproducible evidence before being surfaced, dramatically reducing false positives and false negatives and enabling security teams to prioritize exploitable risk rather than theoretical exposure.
While RidgeGen is designed to be model-agnostic and supports flexible deployment options, including on-premises environments, its development and benchmark testing framework were built on Google Cloud. Throughout the development process, Gemini served as a key baseline model for evaluating performance, reliability, and cost efficiency. Our benchmark testing across multiple leading AI models demonstrated that Gemini delivers a strong balance of performance and cost efficiency, making it a highly effective foundation for enterprise-grade agentic AI security applications.
Key capabilities include:
Autonomous agentic AI red teaming and offensive security testing
Evidence-backed verification that eliminates false positives and AI hallucinations
Discovery of complex multi-step attack chains and business logic vulnerabilities
Click-away remediation guidance generated from validated findings
Runtime guardrails that enforce safe, supervised and authorized testing modes
Secure credential handling through SafeBox, preventing sensitive data from entering AI model prompts or memory
Model-agnostic architecture supporting leading commercial and self-hosted AI models
On-premises deployment options that keep customer data and evidence within enterprise environments
RidgeGen complements RidgeBot, Ridge Security’s AI-powered continuous security validation platform. Together, the two solutions provide organizations with continuous visibility across their attack surface while delivering deep, autonomous validation of high-value targets – creating a unified “find, prove and fix” approach to Continuous Threat Exposure Management (CTEM).
The platform is available immediately for enterprise organizations and managed security service providers worldwide.
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