Project Aries

Multi-Agent Reasoning Swarm

Specialized AI agents collaborate and adversarially challenge one another to perform cross-file reasoning, long-horizon vulnerability discovery, and context-aware security decisions. Unlike standard SAST and DAST tools that flag issues in isolation, Aries understands intent, data flow, and regulatory impact across your entire codebase — exceeding what senior human auditors can achieve.

Eight-Phase Deep Audit Pipeline

A deterministic, multi-pass engine mirroring how elite security teams operate: reconnaissance builds a structural and architectural map of the application (application DNA), identifying trust boundaries, entry points, and sensitive subsystems. Then privacy impact assessment, semantic taint tracking, regulatory compliance audit, contextual SAST with risk prioritization focused on high-risk zones like auth, payments, and APIs, a recursive cross-module reasoning loop, synthetic attack chaining, and hyper-report generation.

Privacy & Semantic Taint Tracking

Automatically detects PII and PHI, evaluates exposure paths, and assesses privacy violation likelihood. Traces untrusted data from UI inputs through business logic to dangerous sinks — databases, logs, network calls — going beyond pattern matching to understand how data flows, transforms, and reaches critical systems across files and modules.

Regulatory Compliance Intelligence

Maps code behavior directly to GDPR Art. 32, HIPAA §164.312, and SOC 2 controls, transforming abstract regulations into enforceable, testable requirements. Each vulnerability is linked to specific legal articles with potential fines and financial liabilities quantified in real terms. Privacy-by-design is enforced through native sensitive data detection — making reports actionable for engineering, security, legal, and executive stakeholders.

Synthetic Attack Chaining

Constructs realistic, multi-step exploit paths that simulate how real attackers pivot across weaknesses — instead of treating findings in isolation, Aries chains vulnerabilities to reveal attack surfaces only visible when logic is combined. Active proof-of-concept generation safely demonstrates exploitability for each discovered chain.

Verified Remediation with Developer Approval

Goes beyond detection into proof, fix, and verification. A dual-agent model proposes fixes while a Shadow Auditor adversarially attempts to break them. Only verified patches are delivered as clean, batched GitHub pull requests for developer review — nothing ships without human approval. Auto-generated unit and integration tests are included for CI/CD validation.

GAD War Room

A real-time Generative Adversarial Defense simulation pitting Red Team AI agents against Blue Team agents to uncover zero-day vulnerabilities pre-production. This continuous adversarial process stress-tests your application's defenses before real attackers can — transforming Aries from a scanner into a proactive security engineer.

Zero-Config Instant Deployment

Start auditing immediately with a pre-trained master security intelligence. No API keys, no onboarding friction, no configuration required. Designed to slot directly into modern DevSecOps pipelines with CI/CD-friendly, scalable serverless architecture — ideal for fast-moving teams, emergency audits, and security triage.

Traditional SAST and DAST tools scan files in isolation using pattern matching. Project Aries uses a Multi-Agent Reasoning Swarm that performs cross-file reasoning, understands intent and data flow, chains vulnerabilities into realistic attack paths, and generates adversarially verified fixes — operating more like an elite security team than a static scanner.

Aries executes eight sequential phases: reconnaissance and application DNA mapping to identify trust boundaries, entry points, and sensitive subsystems; privacy impact assessment with automatic PII and PHI detection; semantic taint tracking from inputs to dangerous sinks; regulatory compliance audit against GDPR, HIPAA, and SOC 2; contextual SAST with risk prioritization focused on high-risk zones like auth, payments, and APIs; a recursive cross-module reasoning loop; synthetic attack chaining to simulate real attacker behavior; and hyper-report generation translating findings into financial and regulatory risk.

Project Aries maps code behavior directly to GDPR (including Art. 32), HIPAA (including §164.312), and SOC 2 controls. Each finding is linked to specific legal articles with potential fines and financial liabilities quantified in real terms. Privacy-by-design is enforced through native detection of sensitive data categories — making reports actionable for engineering, security, legal, and executive stakeholders.

No. Aries proposes fixes through a dual-agent model: one agent generates the fix while a Shadow Auditor adversarially attempts to break it. Only verified patches are delivered as clean, batched GitHub pull requests for developer review — nothing ships without human approval. Auto-generated unit and integration tests are included for CI/CD validation.

The Generative Adversarial Defense War Room is a real-time simulation where Red Team AI agents attack your application while Blue Team agents defend it. This continuous adversarial process stress-tests your defenses and uncovers zero-day vulnerabilities pre-production — before real attackers can.

Aries automatically detects personally identifiable information (PII) and protected health information (PHI) across your entire codebase, evaluates exposure paths, and assesses the likelihood of privacy violations. This feeds directly into the regulatory compliance engine, linking privacy risks to specific GDPR and HIPAA requirements with quantified financial exposure.

Aries traces untrusted data from UI inputs through business logic to dangerous sinks like databases, logs, and network calls. Unlike simple pattern matching, it understands how data flows, transforms, and reaches critical systems — uncovering vulnerabilities that only appear when logic across multiple files and modules is combined.

Instead of treating vulnerabilities in isolation, Aries constructs realistic multi-step exploit paths that simulate how real attackers pivot across weaknesses. It chains findings across files and modules to reveal attack surfaces only visible when logic is combined, with active proof-of-concept generation that safely demonstrates exploitability for each discovered chain.

No. Project Aries offers zero-configuration instant deployment with a pre-trained security intelligence. No API keys or onboarding steps are required — teams get immediate security value, making it ideal for emergency audits, fast-moving teams, and DevSecOps triage.

Aries produces executive- and legal-grade hyper-reports that translate every vulnerability into financial exposure, regulatory fines, and quantified business risk. Each finding maps to specific compliance controls with estimated liabilities — usable by engineering, security, legal, and executive stakeholders.

Project Aries is available on iOS, Android, and Web. It is designed to slot directly into modern DevSecOps pipelines with CI/CD-friendly architecture and scalable execution.

Project Aries is built for any team that wants to prevent breaches, reduce regulatory risk, and ship faster without compromising security — from startups to enterprise security teams. It replaces fragmented SAST, compliance, and audit workflows with a single continuous security intelligence layer that operates at machine speed.