How It Works - Neuralsec

The Problem

Why Static Falls Behind

Traditional security tools focus on moments:

A scan

A pull request

A release

An audit

But risk doesn't live in moments. It emerges as systems change.

The Root Cause

Design assumptions drift, controls decay silently, and exposure accumulates long before alerts fire. When understanding isn't continuous, security teams are always reacting too late.

The Shift

From Snapshots to Continuous Understanding

Traditional tools

Static diagrams that go stale

One-time reviews

Point-in-time findings

Neuralsec

System understanding that evolves with the code

Grounded in architecture, flows, and implementation

Risk tracked as systems change

This continuous understanding enables validated security decisions — not just alert generation.

How It Works

Creating Continuous Understanding

01

Code is parsed and structured into a system-aware model

Neuralsec ingests every repository and builds a semantic model of the code — every function, class, endpoint, and data flow — capturing how components relate to each other and to the system as a whole.

→ A live, queryable understanding of how the codebase is actually structured.

02

Code is understood in system context

As repositories evolve, Neuralsec understands what components do, how data and control move across the system, and where security-critical logic lives.

→ System understanding stays aligned with real implementation.

03

Risk is reasoned about in context

Neuralsec continuously analyzes findings, code changes, and drift between intended design and actual implementation to determine whether change creates real exposure.

→ Not just "is there a vulnerability?" but "does this create validated risk?"

04

Risk exposure is tracked over time

Neuralsec maintains a live view of exploitable paths, business-critical attack surfaces, and control effectiveness as systems evolve.

→ Know what changed, where exposure increased, and whether risk was actually reduced.

Beyond Code

Security Intent Has to Be Validated, Not Assumed

Every team has security intent — controls expected to be in place, sensitive data flows expected to be protected, authorization expected to gate the right endpoints.

But intent isn't enough on its own. As systems evolve, the gap between what teams intended and what the code actually does widens silently.

Neuralsec continuously validates whether security intent still holds in the current implementation:

Confirms whether controls are reachable in the paths that matter

Surfaces authorization gaps where business-critical logic lives

Flags when sensitive data flows through code without expected protection

Security validation does not end at code review. It must remain aligned as systems evolve.

What It Enables

From understanding to validated security outcomes

See real risk, not noise

Reveal hidden attack paths and design-level flaws — including logic and authorization issues traditional tools miss.

Validate what is exploitable

Confirm reachability and impact before engineers act — not just theoretical risk.

Focus effort where it matters

Prioritize based on real exposure and business impact — not raw severity.

Fix with full context

Deliver remediation grounded in architecture, data flows, and system behavior.

Know risk is actually reduced

Track exposure after fixes and verify that the system is safer.

Integration

Built to Fit Existing Workflows

Neuralsec integrates into the tools teams already use. It enriches existing security signals with system-level understanding, without introducing new processes or manual upkeep.

No static snapshots

No additional gates

No manual upkeep

Continuous reasoning across the SDLC

Just continuous security reasoning embedded across the SDLC.

Outcomes

From Alerts to Answers

When security understanding is continuous:

Noise drops

Decisions accelerate

Ownership becomes clearer

Exposure is visible

Assurance becomes provable

Move from reacting to alerts to managing real risk with confidence.

In Practice

What Continuous Understanding Looks Like

Continuous understanding isn't an abstraction — it shows up in concrete artifacts security and engineering teams use every day. Three examples of what Neuralsec produces from the model it builds of your system.

Validated Triage

Every finding gets a verdict grounded in how the code actually works — exploitability, reachability, and business context behind the decision. The reasoning is auditable, not a black-box score.

Risk Verdict

Validated

Confidence

85%

P0

Severity Critical

Missing object-level authorization on the staff reservation management endpoint. The reservation mutation path writes directly to Reservation and ReservationRequest models without validating that the authenticated staff user is entitled to the specific account being modified.

Exploitability Checks

Reachable

API Invoked

Prod Relevant

Exploitable

Compensating Controls: None

CWE: CWE-862 Missing Authorization

OWASP: Broken Object-Level Authorization

Compliance: PCI-DSS GDPR

Impact: Authenticated staff users with the global account_admin permission can create or modify reservations — including payment-affecting changes — for arbitrary account IDs. An attacker (or compromised staff account) can cause financial loss, alter customer assignments, or corrupt reservation integrity across customer accounts. The same authorization gap exposes payment flows the codebase already protects under PCI-DSS controls and customer PII covered under GDPR Article 32.

Remediation in Context

For validated findings, Neuralsec drafts a fix grounded in the code's purpose — then verifies through merge that the exposure is actually closed. Fixes are PR-ready, not snippets to paste.

Patch Creator Draft

PR Draft Ready

Linked Verdict Missing Object-Level Authorization · Reservation Management Endpoint

Patch Type Authorization enforcement · 2 files modified

Reasoning Summary

The reservation mutation path lacked object-level authorization. The fix introduces an entitlement check against the authenticated staff user's accessible accounts before any reservation write — using the existing AccountEntitlementService already present in the codebase, reusing established patterns from adjacent endpoints rather than introducing new infrastructure.

Code Change

Diff ReservationService.kt · L42

fun updateReservation(staffUser: StaffUser, request: UpdateReservationRequest) {

+ accountEntitlement.assertCanModify(staffUser, request.accountId)

reservationRepository.update(request.reservationId, request.changes)

}

✓ patch generated · 287 ms

Context Applied

Authorization pattern: AccountEntitlementService (used in 14 adjacent endpoints).
Permission model: aligns with existing account_admin and account-scoped role hierarchy.
Test impact: existing entitlement tests cover the new assertion path.
Breaking change: none — staff users without entitlement previously had implicit access.

Verification

Pre-merge: patch passes existing test suite.
Post-merge: re-analysis confirms the original Risk Verdict no longer reachable.
Coverage:6 sibling endpoints checked — none affected.

Compliance Closes controls under PCI-DSS Req 6 (secure systems) and GDPR Art. 32 (security of processing).

Compliance Applicability

Neuralsec maps how each finding touches regulatory obligations — GDPR, SOC 2, PCI, EU AI Act — and updates the applicability map as code changes. Audit context is built from the code itself, not maintained by hand.

PCI-DSS High applicability

Cardholder data flows through this codebase across many endpoints. Payment provider integration, direct token handling, and webhook processing are all in scope of PCI-DSS — alongside the secrets and audit trails those flows depend on.

Detected Signals

payment intents card tokens webhook handlers stored secrets refund flows

Relevant Controls

Req 3 Protect stored cardholder data — limit exposure of secrets and tokens
Req 6 Develop and maintain secure systems and applications
Req 10 Track and monitor all access to cardholder data
Req 12 Maintain an information security policy and risk assessments

GDPR Medium applicability

Personal data — identifiers, billing details, KYC fields — is processed across many endpoints with read/write paths to storage and event streams. Some flows persist without consistent masking, raising the bar on access and retention controls.