Evidence and source history
Preserve what the AI used, where it came from, whether it stayed intact, and how it moved through the workflow.
AI Action Assurance
Make AI actions reviewable, approved, and accountable.
Connect the evidence, the human decision, and the permitted action.
Bring policy, supporting evidence, independent review, approved purpose, and the recorded outcome into one Trust Record. Start with a defined workflow and make every boundary visible.
AWS Partner validation completed
AWS solution review completed
AWS-first architecture
Enterprise pilotA focused path from one workflow to a reviewable operating model.
Duration30–45 days
Scope2–3 AI workflows
Deliverables4 customer-ready outputs
Book the briefing
The enterprise control gap
AI can act. Can your organization prove the action was valid?
Security tools show activity. Monitoring tools show behavior. Governance programs define policy. Organizations also need one clear record that connects the evidence, checks, human approval, approved purpose, known limits, and final outcome.
01What happened?Who and what acted
02What evidence supported the outcome?source history
03What verification occurred?Checks performed
04Who approved this use?Human approval
05Can the workflow be reconstructed later?Review history
The category
AI Action Assurance, from evidence to outcome.
DigiTrust brings five controls into one review path. The reference implementation makes each check explicit; production acceptance verifies the path in its actual deployment.
Repeatable, rule-based checks
Use clear rules to check evidence quality, supporting facts, approval rights, conflicts, safe disclosure, and the exact information reviewed.
Independent human approval
Bind a separate, authorized reviewer to the exact result, intended purpose, destination, conditions, and approval window.
Approval for a specific use
Recheck the approved result and conditions before use. The reference controls reject changed, expired, withdrawn, or substituted requests.
Outcome and recovery evidence
Keep completed, blocked, and uncertain outcomes distinct. An uncertain response requires inspection of durable records before any further action.
Reference workflows
See the review path in two practical workflows.
Synthetic examples connect the assessment, architecture, approval boundary, and recovery checks. They support a focused design discussion before customer data or production systems are introduced.
Referral intake
Check the evidence behind an intake summary, resolve missing support, and obtain independent approval for a specific receiving queue.
- Reviewed action
- One referral handoff
- Critical boundary
- The approved result and destination
- Recovery question
- Did the handoff already complete?
Documentation evidence
Connect a draft to its supporting sources, preserve corrections, and let an accountable reviewer approve the exact evidence packet for a review queue.
- Reviewed action
- One evidence packet handoff
- Critical boundary
- Source support and independent review
- Recovery question
- Can the recorded outcome be recovered?
Synthetic reference examples. No customer deployment, patient outcome, clinical decision, or external system write is implied. See implementation and release boundaries.
Regulated-industry launch
Built for AI workflows where evidence and authority matter.
Start with the workflows that already carry customer, regulatory, clinical, security, or mission consequences.
Make financial AI reviewable.
Strengthen the evidence and approval record across banking, insurance, risk, fraud, customer service, cybersecurity, AI assistants, and automated workflows.
- Fraud and financial crime
- Risk and decision support
- AI copilots and agents
Make healthcare AI reviewable.
Add a clear evidence and approval record to clinical governance and human review, with known limits and a complete history.
- Referral intake and handoff
- Documentation evidence review
- Digital and operational AI
Preserve human authority in high-consequence operations.
Begin with unclassified enterprise, engineering, knowledge, and mission-support workflows under explicit data boundaries.
- Knowledge and engineering support
- Enterprise automation
- Decision-assistance workflows
The first enterprise engagement
Start with two or three consequential AI workflows.
The DigiTrust Enterprise AI Evidence and Assurance Pilot is a focused 30- to 45-day engagement that turns governance intent into a reviewable operating model.
DigiTrust Enterprise AI Evidence and Assurance Pilot
Evaluate current controls, map the evidence and authority path, identify limitations, and define a production expansion plan without exposing sensitive data in the initial discovery process.
Duration30–45 days
Scope2–3 AI workflows
Commercial termsProvided through AWS and direct sales channels
PurchasingAWS Marketplace
Customer deliverables
- 1Workflow and control assessment
Current evidence, governance, authority, review, and operational boundaries. - 2Evidence and assurance map
Sources, checks, actors, approvals, approved-use conditions, and limitations. - 3Production roadmap
Prioritized architecture, operating model, success criteria, and expansion path. - 4Executive findings
A customer-safe summary for security, risk, compliance, purchasing, and leadership.
AWS credibility and purchasing
AWS validation completed. Available for customer purchase.
DigiTrans completed AWS Partner software validation, DigiTrust passed an AWS solution review, and the product is publicly listed in AWS Marketplace.
01Partner validation complete
DigiTrans completed an AWS Partner software validation milestone.
02Solution review passed
DigiTrust passed an AWS review of its solution foundation.
03Marketplace live
An active customer purchasing path is available through AWS Marketplace.
AWS participation and Marketplace availability support customer purchasing and sales collaboration. They do not mean AWS endorses DigiTrust or guarantees any customer, security, regulatory, or business outcome.
Why DigiTrust
A consistent review path as your architecture evolves.
Model choice and deployment location are separate architecture decisions. DigiTrust is developing provider interfaces around a common evidence and approval contract, with AWS as the first deployment profile.
Identity, evidence integrity, approval rights, conflicts, safe disclosure, and approved-use checks cannot be skipped because an AI answer sounds persuasive.
Agents cannot approve their own consequential output or transfer approval to a changed result.
Changing a model does not establish support for another cloud. Each identity, storage, key, execution, and recovery adapter needs its own evidence.
Missing evidence, unresolved concerns, expired approval, and future capabilities are stated clearly instead of being hidden.
Current stage: Reference controls and synthetic workflow simulations are available for review. Production expansion requires approved integrations, reviewed protection, recovery evidence, and a recorded release decision. Customer legal, clinical, security, audit, and regulatory responsibilities remain with the customer.
The next step
Choose one consequential AI workflow. Make it reviewable.
Request a 30-minute Enterprise AI Evidence and Assurance briefing, define the right pilot scope, or begin through the AWS Marketplace purchasing path.