Supercharge agents with target context
Hand agents your architecture, your policies, and the risks you already accepted. They apply it on the next run and stop re-reporting decisions you made on purpose.
How it works
Agents test from an attacker's position by default. Context is how they learn what you already handled.
Upload what you have
Network diagrams, API specs, architecture reviews, security policies, previous pentest reports. Agents organize it themselves, per target or across the whole team.
Agents apply it while testing
With your context in hand, agents skip the ground you've already covered and spend that effort probing deeper, surfacing more of what you haven't caught yet.
Triage more effectively
Agents use your context to score vulnerabilities more accurately.
Agentic Control System
As agents remediate beyond code, into cloud configs, network policies, and infrastructure, you need a system to track what they changed and why. The ACS is a git-like control plane for every change agents make across non-code surfaces.
Version control
Every agent-made change is versioned with full before/after state, so you always know what changed and can roll back.
Approval workflows
Route changes through your existing approval process. Agents propose, your team approves, agents apply.
Full audit trail
Complete history of every remediation action across every surface, who, what, when, and why. Built for compliance.
Driving down MTTR
Agents find and fix issues in minutes. The ACS gives you the control to let them move fast without losing visibility.
Security that gets better the longer it runs
MindFort agents don't start from scratch every time. They accumulate knowledge about how your organization works, your tech stack, deployment cadence, configuration patterns, and defensive posture. Every cycle produces better results than the last.
Environment-aware testing
Agents map how your teams build, deploy, and configure systems, tailoring their testing and remediation to your specific stack and conventions.
Continuous context building
Every operation deepens an agent’s understanding of your environment. Past findings, infrastructure changes, and deployment patterns all inform future runs.
Adaptive attack strategies
Agents remember what worked and what didn’t. They evolve their approach based on your specific defenses, getting sharper with every cycle.
Efficient at scale
Self-learning means agents spend less time re-discovering what they already know, more targeted testing, faster remediation, better coverage across your entire stack.
