A multi-agent platform that reasons about security the way an experienced security team would.
Findings get reasoned about, not just listed. Fixes get drafted, not just suggested. Risk gets verified, not just flagged.
Scanners produce findings. They don't understand whether those findings represent real risk in your specific system, whether existing controls already mitigate them, or whether the code path is reachable from any external entry point.
AI generates more code than human teams can review. Scanners produce more findings than engineers can triage. As foundation models commoditize detection itself, the bottleneck shifts from finding vulnerabilities to maintaining alignment as systems continuously change.
At the same time, attackers are accelerating. Time-to-exploit has compressed from weeks to hours, and the gap between disclosure and weaponization keeps shrinking.
The hard problem isn't detection. It's understanding what's drifting, reasoning about what matters, and acting at the speed code now ships — and the speed at which it now gets attacked.
Security fails when no one understands the system end-to-end.
Most AppSec tools are scanners with AI features layered on top. Neuralsec is the inverse — a coordinated system of specialized agents, each purpose-built for a specific reasoning task, all reasoning over a persistent semantic understanding of your code.
This is what AI-native AppSec actually looks like at the architectural level. Not features bolted onto a scanner. A platform designed agent-native from the foundation.
Each agent is purpose-built for a distinct reasoning task. All coordinate through your codebase's shared semantic foundation.
Reasons about every finding's actual exploitability against your system context. Distinguishes real risk from theoretical pattern matches. Output is a verdict with documented reasoning, not a severity score.
Drafts PR-ready fixes grounded in the code's business purpose and surrounding architecture. Uses the Triager's reasoning plus the full code understanding layer. After merge, verifies the exposure is actually closed.
Reasons about system-level architecture and the top threats specific to how your application is built and deployed. Continuous threat reasoning grounded in current code, not a manual STRIDE exercise translated into software.
Recognizes when findings from different scanners describe the same underlying issue. Engineers don't waste cycles re-triaging the same thing under different names.
Validates dependency vulnerabilities by reasoning about actual reachability through the code understanding layer. Most SCA findings can be safely deprioritized after this analysis.
The agents don't run in isolation. Each operates over the same persistent understanding of your code, and the system gets sharper for you the longer it runs.
Five agents today, with more in development.
From understanding to outcomes.
Most AppSec tools produce a list. Neuralsec produces a pipeline. The Triager reasons about each finding's exploitability against your system context. The Patch Creator drafts contextual fixes for the findings worth fixing. After merge, the system re-validates to confirm the exposure is actually closed.
Every verdict carries documented reasoning. Every patch comes with the system context that produced it. Every fix gets verified after merge.
Illustrative pipeline based on observed engagement data. Actual flow proportions vary by codebase, scanner mix, and engagement maturity.
The Triager validates every finding against your system context — what's reachable, what handles sensitive data, where compensating controls already exist. Findings arrive as reasoned verdicts, not severity scores.
Pattern-matching tools miss the flaws hiding in how your system actually works — broken authorization, business logic gaps, missing access controls. Neuralsec's code understanding layer detects these issues directly, not by waiting for someone else's scanner to flag them.
The Patch Creator drafts contextual fixes as PR-ready changes. After merge, the system re-analyzes to confirm the exposure is actually closed. Risk reduction gets verified, not assumed.
The same code understanding that powers triage and remediation maps each finding to GDPR, SOC 2, PCI, and EU AI Act — continuously, as regulations evolve and your code changes. Compliance is an output of the analysis, not a separate workstream.
Each analysis enriches your code understanding layer. The platform gets sharper for your specific codebase as it runs. This isn't a tool you replace — it's a substrate that deepens.
Neuralsec integrates into your existing workflows. No manual upkeep. No static snapshots. Just continuous security understanding.
Neuralsec doesn't just reduce security work — it reduces uncertainty.
Not point-in-time analysis. Risk is evaluated in the context of how your system is actually built and evolving.
Design decisions are validated against real code, data flows, and control enforcement — not assumptions.
Security decisions are based on reachability, exploitability, and system context — not static signals.
Exposure is continuously updated as systems change, so teams know when security improves — or silently degrades.
Most AppSec platforms add AI features to existing pattern-matching scanners. Neuralsec is multi-agent from the foundation, with specialized agents reasoning over a shared semantic model. Every new capability extends the same foundation rather than requiring a parallel pipeline.
Modern software systems evolve faster than humans can reason about their security. As designs drift, assumptions break, and code changes accumulate, risk emerges silently across systems.
Despite more scanners, alerts, and automation, security understanding has not kept pace with how software actually evolves.
Neuralsec exists to restore that understanding — and ensure it translates into continuously validated security and compliance.
We build a persistent system-level understanding of how applications are designed, implemented, and changing — so teams can see real exposure, validate risk with confidence, and keep security aligned as software evolves.
Security should not rely on snapshots or guesswork.
It should be grounded in continuous system understanding.
Designing complex systems with security built-in
Moving fast under regulation
Who need continuous assurance, not reports
See how continuous security reasoning works in practice.