Pattern-matching scanners miss what depends on context: tainted input that crosses three files, authorisation that fails only on one call path, secrets assembled at runtime. Our proprietary platform performs deep semantic analysis of source code with AI, maps every finding to CWE and OWASP, and runs where you decide: as a managed SaaS or entirely inside your own infrastructure.
The platform reads code the way an expert reviewer does. It follows logic flow, resolves variable scope and reasons about what a function is for, so that a value sanitised on line 14 is still recognised as unsafe on line 92 when a later path transforms it back. The result is the depth of a manual code review, repeatable on every commit, at machine speed.
Analysis runs on our own models by default. Where a client prefers, the platform can use frontier models such as Claude Opus, Claude Fable or GPT under the client's own agreements, with the same workflow and the same evidence. Source code is processed only for the duration of a scan and discarded afterwards; the findings persist and belong to the client.
The platform follows data across files and functions, understands what each transformation does and reports the path that makes a finding exploitable. Every finding carries its severity, CWE and OWASP category and a proposed fix that your engineers review before anything changes.
Input sanitised on line 14 is re-expanded by normalize(legacy=True) on line 58 and reaches a shell on line 92. Exploitable by any authenticated user with export rights.
A managed tenant operated by Aureon, or a full installation inside your own infrastructure. Our proprietary models by default; frontier models such as Claude Opus, Claude Fable or GPT when you prefer them, under your own agreements. In every case the code is processed only for the scan and discarded afterwards.
Every repository gets a severity-weighted score and a risk grade. Mean time to remediation is computed for critical and high findings, leaderboards rank products and teams, and every number is available through the API for executive dashboards and audit evidence.
| Repository | Grade | Critical | High | Trend |
|---|---|---|---|---|
| payments-api | A | 0 | 2 | |
| customer-portal | B | 1 | 5 | |
| infra-terraform | C | 2 | 3 | |
| legacy-billing | D | 4 | 11 |
Taint tracking across files and modules, authorisation flaws in business logic, command injection through templated configuration, secrets constructed at runtime and weak cryptography applied to the wrong data: the classes of vulnerability that signature-based tools cannot see.
Proprietary models tuned for vulnerability analysis by default. Optionally, frontier models such as Claude Opus, Claude Fable or GPT, used under your own contracts and keys, so that you decide which AI reads your code and under which terms.
A managed tenant operated by Aureon, or a full deployment inside your data centre or private cloud with the same features and the same reports. With our own models the platform also runs in isolated environments without internet access. Source code never has to leave your perimeter.
Every finding carries severity, the affected file and line, the data path that makes it exploitable, its CWE and OWASP category and a proposed fix with explanation. The platform proposes; your engineers decide and apply.
Filter by severity, file, CWE, age or status. Suppress false positives and accepted risks with a written rationale and an expiry date, so that decisions are documented and never silently stale.
Trigger scans from GitHub Actions, GitLab CI, Jenkins, Azure DevOps, Bitbucket Pipelines or any orchestrator through the API. Results flow to Jira, Slack, Teams, your SIEM or any webhook. Scheduled scans and incremental analysis keep continuous assurance affordable.
Findings mapped to CWE and OWASP, trends over time, timestamped exports and a full audit log of who scanned, suppressed or changed what. Ready for ISO/IEC 27001, SOC 2, NIST SSDF, PCI DSS, DORA and NIS2 reviews.
A security score per repository and per team, mean time to remediation for critical and high findings, risk grades and leaderboards, all available through the API for board decks and executive dashboards.
Repositories are onboarded from GitHub, GitLab, Bitbucket, Azure DevOps or self-hosted Git, with per-repository access keys and branch selection. For on-premises deployments, the platform is installed inside your environment first.
A deep inspection of the selected repositories establishes the initial findings, the security score and the risk grade of each codebase, reviewed with your engineers before anything is reported upwards.
Pipelines and schedules keep every tracked branch under review. Incremental analysis reuses results for unchanged files, so large monorepos stay fast and affordable.
Engineers triage in the results view, request fix proposals and verify closures with scan-to-scan diffs. Compliance exports and trend reports document the improvement.
Clear, usable outputs for executives, technical teams, insurers and regulators.
More than 180 languages and formats, from C, C++, Java, C#, Go, Rust, Python, JavaScript and TypeScript to COBOL, CUDA, Solidity, SQL dialects, Terraform, Kubernetes manifests, Helm, Ansible, Dockerfiles and security rule formats such as YARA and Snort.
No. Code is processed only for the duration of a scan and discarded afterwards. It is never used to train, fine-tune or evaluate any model, and when frontier models are used they run under your own agreements with the provider.
The analysis pipeline treats source code strictly as data, never as instructions. Inference is stateless and bounded, and the workers run in network-isolated containers with no outbound access, so a model confused by hostile input has nowhere to send anything.
Yes. Scan profiles range from fast to deep inspection, and incremental analysis reuses prior results for files whose content has not changed, which on a large repository with a small change cuts the work by 90 percent or more.
Never. The platform proposes fixes with explanation and context; your engineers decide what is applied. Nothing is committed automatically.
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