Neuralsec is an AI-native Product Security platform that detects vulnerabilities, validates real risk, drives remediation, and assures production changes using a continuously updated understanding of your system.
It combines native detection with the security signals you already have, so findings and changes are evaluated against the implementation, controls, data flows, and previous security decisions that actually matter.
From security signal to evidence-backed decision and safer outcome.
Detect vulnerabilitiesValidate real riskRemediate & assure changes
A reported finding becomes an evidence-backed security verdict.
Neuralsec combines existing security signals, native implementation-aware detection, persistent system context, and retained evidence to drive decisions across findings, risks, remediation, and production changes.
Neuralsec builds and maintains a shared model of your system - its architecture, entry points, data flows, sensitive operations, trust boundaries, dependencies, and the controls that protect them.
See how repositories, services, infrastructure, and external dependencies connect across the system.
Built from the ground up as an AI-native platform, Neuralsec's agents share architecture, data flows, organizational context and security memory, security decisions, and remediation history instead of operating in isolation.
Each investigation builds on what Neuralsec already understands about the system and the organization, helping agents make more consistent security decisions over time.
Specialized Agents
One shared understanding powers detection, investigation, threat modeling, prioritization, remediation, validation, dependency analysis, compliance, and security guidance.
Neuralsec performs native implementation-aware analysis across entry points, controls, data flows, sensitive operations, and trust boundaries - then investigates those results alongside the security signals you already have.
Find authorization gaps, unsafe trust-boundary transitions, risky data flows, and other vulnerabilities that require understanding how the application actually behaves.
Runs on the maintained system and threat-model context, reasoning across entry points, controls, data flows, sensitive operations, and business behavior - not isolated patterns alone.
Keep the tools that work. Use Neuralsec where security decisions require understanding the implementation, not just matching a pattern.
Neuralsec validates findings against the real implementation - checking reachability, exploitability, effective controls, business purpose, and downstream impact before deciding whether a reported issue represents real exposure.
Real risks are promoted with supporting evidence. Findings that are already mitigated or not exploitable as reported are reduced without asking engineering to fix what is not actually broken.
Resource authorization missing. Caller-controlled identifier reaches a sensitive operation.
Session-bound identity and ownership already mitigate the reported attack path.
Models reason. Evidence decides.
Model output can explain and assist. Retained implementation evidence grounds the security decision.
Neuralsec groups related findings and prioritizes them using exploitability, reachability, affected business capabilities, sensitive data, existing controls, and system context.
Instead of another severity-ranked backlog, security teams get a focused view of the exposures most likely to create real business impact.
See which mechanisms and missing controls are driving repeated exposure across the organization.
From isolated findings to system-level risk.
Neuralsec analyzes how a service handles sensitive data, exposes functionality, and connects to the rest of the system to determine which regulations and security requirements are likely to apply.
Instead of treating compliance as a separate point-in-time exercise, Neuralsec connects applicability and supporting evidence to the implementation and risks that actually exist.
A service processes payment data and exposes transaction APIs, increasing PCI DSS applicability and linking it directly to the relevant flows and security controls.
Compliance grounded in system behavior, not questionnaires alone.
Neuralsec generates contextual remediation grounded in the application's architecture, data flows, existing security patterns, tests, and organizational conventions.
Rather than producing an isolated code suggestion, Neuralsec identifies the control or implementation change needed to remove the exposure, then validates whether the proposed change actually addresses the original security condition before it reaches production.
Patch Validation checks security coverage, build evidence, behavioral impact, and operational or compliance consequences before the change is considered ready for review.
Evaluate security coverage, build evidence, behavioral impact, and operational risk before the patch is merged.
Contextual fixes. Delivered where engineering already works.
Confirm the proposed change addresses the original security condition, passes available build and test evidence, and does not introduce obvious new risk.
Neuralsec evaluates production-bound changes against system context, prior security decisions, sensitive operations, findings, dependencies, and controls - then produces evidence and focused review guidance according to your policy.
Policy decides the approval. Neuralsec provides the evidence and focus.
Neuralsec's Security Advisor reasons across findings, architecture, data flows, business context, security controls, compliance applicability, and organizational knowledge to help teams investigate risk and improve how security decisions are made.
Find systemic security weaknesses across repositories, findings, and previous decisions.
Every investigation, remediation decision, and verified outcome enriches the organizational security contextused by future analyses.
Guidance derived from one failure shapes the code written next.
Support varies by tool and integration.
Neuralsec connects security investigation, remediation, and verification with the tools your teams already use - from development and issue tracking to security testing and collaboration.
Bring external security findings into the same investigation and remediation loop, and push validated work back into the systems teams already use.
Through MCP, Neuralsec can make its shared system model, findings, architecture, exposure paths, organizational guidance, and previous security decisions available to compatible coding agents and developer tools.
The agent brings the coding capability. Neuralsec brings the security context.
One security context. Available to teams, tools, and agents.
The same Neuralsec platform powers both models. The difference is who operates the security workflow.
For teams that want to operate the workflow themselves.
For organizations that need continuous Product Security coverage without building a dedicated AppSec team.
Neuralsec is being validated with design partners across real application-security environments - from repository onboarding and system understanding to investigation, remediation, and verification.
Analyze real application code, architecture, organizational context, and security signals.
Work through the tools security and engineering teams already use.
Follow risk from investigation through remediation and post-fix verification.
Built for AppSec and Product Security teams securing complex, fast-changing software systems.
Connect your repositories and security signals to see how Neuralsec investigates risk, prioritizes what matters, and closes the loop through remediation and verification.
See how Neuralsec can work with your security team - or operate the Product Security workflow for you.