Continuous Offensive AI
RidgeGen reasons through your environment the way a human red teamer would: autonomously, at machine speed and with evidence at the end
Autonomous Exposure Discovery
Map the real, reachable attack surface across the target — not just what’s listed, what’s actually reachable.
Vulnerability Validation
Prove what’s genuinely exploitable, with evidence — not a severity score.
Multi-Step Attack-Chain Reasoning
Chain weaknesses together the way a human red team would, not one vulnerability at a time.
Business-Logic & Non-CVE Risk
Surface flaws that scanners and signature-based tools structurally cannot reach.
Click-Away Remediation
Evidence-backed fixes, with the human in control of every change.
Adaptive Replanning
When a path is blocked, RidgeGen re-plans and re-executes in real time.
One Platform, Your Highest-Value Targets
Model-agnostic. Bring your own LLM. Extend the specialist agent pool with your own experts (BYOA).
Web Applications
APIs
Host
Active Directory
AI Systems
Key Differentiators at a Glance
Built for Autonomous Security. Engineered for Enterprise Trust.
Proven So You Can Fix Faster
One set of AI agents discovers potential risks. Another independently validates every finding. The result is evidence-backed, explainable, and repeatable—so your team can remediate with confidence.
Test Agents
Probe the attack surface
Validation Agents
Independently reproduce
Evidence Gate
Promote only with proof
Fix-ready finding
Detailed remediation steps
Autonomous AI. Deterministic Control.
Guardrails enforce every safety-critical policy outside the model’s discretion, creating behavior that’s consistent, inspectable, and provable. SafeBox keeps credentials out of prompts, memory, and evidence—protecting sensitive data while enabling fully autonomous security validation.
FIXED SAFETY ENVELOPE | DESTRUCTIVE ACTIONS ALWAYS DENIED
SAFE
Production default. Non-intrusive validation.
SUPERVISED
Validation default. Necessary actions to show proof.
AUTHORIZED-AGGRESSIVE
Signed authorization, non-production only.
SafeBox credentials applied at the tool boundary, outside the model’s view. Never in prompt, memory, or evidence, at every posture above
Agentic Security That Adapts to Your Enterprise.
RidgeGen deploys within your existing environment, integrates with the workflows your teams already use, and builds on your organization’s knowledge—while keeping sensitive data, credentials, and IP inside your boundary.
On-Premise. Model-Agnostic.
Deploy within your security boundary while keeping sensitive data, credentials, and IP under your control.
Fast Deploy. Easy Integration.
A small footprint connects to existing DevSecOps workflows with minimal operational friction.
Turn Domain Knowledge Into Shared Intelligence.
RidgeGen learns your environment and captures human expertise as enterprise memory that strengthens testing and remediation over time.
Elite Depth, Without Elite Headcount
Augment your red team, don’t replace it
RidgeGen handles the machine-speed exploitation; your team directs it and owns the decision to fix.
Findings you can act on, not assertions
Every result is independently reproduced before it’s shown — not a generic LLM’s best guess.
Reach the risk automated pentest tools can’t
Business-logic and non-CVE vulnerabilities, chained the way a real attacker would find them.
Fix with guidance, not guesswork
RidgeGen provides evidence-backed guidance to fix the code behind each validated finding.
Learn more about RidgeGen
View a RidgeGen Sample Report
Frequently Asked Questions
How do we know a finding is real and not a false positive?
Every finding goes through an autonomous verification process that confirms it with reproducible, exploitable evidence before it’s ever reported to you. Candidate findings are only promoted to customer-facing results when artifacts and evidence actually support the affected target, severity, and exploitability — eliminating the manual triage that comes with scanner false positives.
How does RidgeGen adapt to my environment?
RidgeGen will be deployed as a virtual machine in your enterprise on-premises network, and please make sure it network reachable to your test network or targets. If your organization is AI-ready — meaning you have enterprise API keys to one of the frontier models — you can directly configure the API key in RidgeGen, and RidgeGen is then ready to find risks in your particular environment, report validated results, and offer a click-away remediation plan. Once the security team fixes the problem, they can simply click the re-run button to verify the fix.
If your organization is not AI-ready yet, you can choose to subscribe to a frontier enterprise license or deploy a self-hosted open-source model. Ridge Security recommends multiple models to choose from — please see the details in the data sheet.
How do we make RidgeGen safe for production testing?
Production runs default to the Safe zone, where write, modify, and delete actions against targets are blocked outright — RidgeGen can still surface and prove a risk, but it can’t alter your live systems to do so. These guardrails are enforced outside the model’s own discretion, with any higher-impact action routed through explicit approval and logged for audit, so nothing destructive happens without deliberate, traceable authorization.
Does RidgeGen require a specific AI provider to run?
No. RidgeGen is model-agnostic, letting you choose or switch between supported providers (Anthropic Claude, Google Gemini/Vertex AI, OpenAI, DeepSeek) or self-hosted open-source models entirely within your own environment.
Can RidgeGen run in our own data center, or is it cloud-only?
RidgeGen deploys on-premises as a self-contained software package, keeping testing, findings, and sensitive data fully within your environment. Minimum requirements are an 8-core CPU, 32GB RAM, 200GB storage, and Ubuntu 24.04.4 LTS, supporting up to 4 concurrent testing tasks at a baseline configuration.
How does RidgeGen’s Security Guardrail work?
RidgeGen enforces safety through three operating zones: Safe (Green) permits no write or delete actions against targets and is recommended for production; Supervised (Amber) blocks deletion or modification of existing data but allows temporary, cleaned-up writes where needed to prove a finding; and Authorized-Aggressive (Red) permits write, modify, and delete actions that may be unrecoverable, reserved for lab and pre-production testing. Strictness changes across the zones, but target confinement, auditability, and guardrail enforcement stay fixed no matter which zone is selected.
What happens to our credentials once a test is finished?
Credentials are held in a protected SafeBox and used in memory only for the duration of a test — they are never persisted in RidgeGen and never passed to any third-party system, including model vendors. Anything written to disk is encrypted, and stored evidence is limited to high-level structure or metadata rather than actual credential values or database contents.
Can we extend RidgeGen with our own tools or internal expertise?
Yes — RidgeGen’s open prompt interface lets operators teach it about their specific environment, and that domain knowledge is captured into a shared enterprise memory (the Ridge Knowledge store) that compounds across runs instead of disappearing. It also supports bring-your-own-agent extensibility, provided any custom agent inherits the platform’s authority model, tool boundaries, and verification gates rather than bypassing them.
Proof is good,
a fast fix is better