Project Solace
Helps security and compliance teams evaluate their environment against recognized control frameworks, producing structured findings that a human reviewer confirms before they're treated as resolved. Designed to reduce the manual effort of continuous security self-assessment, not to replace human judgment on risk acceptance.
Product status
- Concept
- Prototype
- MVP
- Pilot
- Beta
- Production
- Scale
Problem
Security and compliance teams spend significant manual effort keeping evidence and controls aligned and preparing for audits.
Why this problem matters
Manual processes don't scale with the pace of change, and gaps are often discovered during an audit rather than before one.
Solution
An AI-assisted evaluation workflow that turns internal evidence into structured findings mapped to recognized control frameworks, with a human reviewer confirming every finding before it's treated as resolved.
Core capabilities
- Control-framework mapping with supporting evidence
- Draft finding generation for human review
- Evidence versioning and audit trail
Architecture
An evidence-processing pipeline paired with an AI-assisted evaluation layer and a human-in-the-loop review step before any finding is marked resolved.
How it works
- 1Bring in policy and configuration evidence from your environment.
- 2The system proposes control mappings and findings grounded in that evidence.
- 3A human reviewer confirms, edits or rejects each proposed finding.
AI capabilities
- Evidence-grounded finding generation, not unsourced claims
- Human-in-the-loop confirmation workflow
Security model
- • Tenant-isolated data storage
- • Audit logging of all AI-generated findings and human confirmations
- • No customer evidence used for model training
Framework mapping supports SOC 2 and ISO 27001 control language (mapping only, not a certification)
Integrations
- • Document upload
- • Planned: cloud configuration connectors
Deployment models
Target customers
- • Enterprise security teams
- • GRC / compliance leaders
- • Managed security service providers
Business model
Planned usage-based SaaS pricing tied to evidence volume and connected sources; to be finalized ahead of pilot commercialization.
Differentiators
- • Every finding is evidence-linked rather than free-form AI output.
- • Built around human confirmation rather than autonomous determinations.
Evidence
Architecture & documentation
Architecture diagram
Available on requestProduct evidence
Prototype walkthrough
Available on requestTraction
Pilot customers
Pre-pilot
No production pilots yet under contract.
Roadmap
now
Core mapping pipeline
Evidence ingestion and citation-grounded mapping for a first control framework.
next
Cloud connectors
Direct evidence collection from common cloud configuration sources.
later
Continuous drift detection
Ongoing re-evaluation of posture as evidence changes.
Team capability
Investor relevance
Illustrates the company's approach to grounded, auditable AI outputs applied to a large and recurring enterprise spend category (GRC and audit preparation).