Noetis Cybersecurity · Business engine
Security Design Review Engine
Defensive review of an authorized system design.
Answers: "Can an engine review an authorized system design for security risks?" For security teams.
Defensive design review of documents you are authorized to share. It does not scan systems or produce exploits.
Available today
ImplementedImplemented: runs on the shared runtime with supplied material. One bounded development check on 2026-10-12 took 151.8s with 16 model calls and ended "incomplete". That is a development signal, not an evaluation. Customer access is by approved account.
What you send
- Question
- Supplied text material
- Text material up to 150,000 characters per run
What comes back
- Defensive assessment and remediation plan.
- Findings with the located quote and a support status from claim review
- Release status: released, incomplete with reasons, or failed
- Executed roles, usage and measured cost
Sample
Authored illustration. Written by Encevra to show the workflow. Not customer data and not a recorded model output.
Review this authorized design for defensive gaps.
System: public web app, admin panel on same domain at /admin, protected by password only. Database backups nightly to the same cloud account, no restore test recorded. Logs kept 7 days. Service account with full admin rights used by CI.
- Documented risks tied to the parts of the design they come from
- Proposed remediations marked for human review
- Assumptions the design leaves unstated
The real response shape is documented in the API reference.
How the workflow operates
Each role is a responsibility in the workflow, served by a third party language model. More roles are not automatically better; depth and routing decide which run.
Models this engine uses: OpenAI (GPT), Anthropic (Claude). Specialists run on OpenAI models and the claim review and final brief on Anthropic models. If a provider has a temporary failure, the step can fall back to an OpenAI model, and the run records that.
Asset and Boundary Mapper
Identify assets, data flows and trust boundaries.
Threat Modeler
Describe plausible abuse and failure scenarios.
Identity Reviewer
Examine permissions, authentication and access assumptions.
Configuration Reviewer
Review supplied configurations and dependency information.
Detection and Recovery Analyst
Assess logging, response and recovery readiness.
Remediation Prioritizer
Rank findings by supported impact and practical fixes.
Evidence and deployment
Evidence
This engine reads the text you send in the request. Each released finding carries the exact quote and its character span in that text, located mechanically before release. Quotes that cannot be located are labelled Insufficient evidence and never shown as source verified. Interpretation is reviewed as a separate step, and gaps such as a missing clause describe what was searched instead of citing text that is not there.
Deployment
Runs on Encevra hosted infrastructure using the third party models listed above. Every model call and fallback is checked against the deployment policy first. Private cloud and air-gapped deployment are planned; discuss deployment requirements.
What it does not handle
- Autonomous scanning
- Changes to external systems
- Offensive tooling
- Uses only supplied material. No freshness guarantee beyond the documents provided.
- Required for every output during trials.
How to integrate
Send "engine": "cybersecurity" with your question and material from your server. Keys stay server side.
How to evaluate it
No published result for this workflow yet. Build a small answer key from your own work and compare against a strong single model baseline. See the evaluation checklist and method.
Cost and rights: pricing and licensing. Showing outputs to your own customers needs a written agreement.
Related engines
- Technical Design and Migration Review API
- Compliance Evidence Mapping Engine
- Due Diligence Document Analysis API
Content reviewed 2026-10-12 by the Encevra engine team. Engine version 0.1.0 on noetis-core-1.


