Sensitive data exposure
Detect and reduce exposure of PII, PHI, PCI, confidential business data, and restricted client data before it reaches AI or external systems.
DigiTrust
Make enterprise AI safe to scale.
DigiTrust is the trust infrastructure layer for enterprise AI, helping organizations control sensitive data use, enforce policy decisions, route human approvals and produce audit-ready evidence.
Policy checked before AI use
Human approval for high-risk workflows
Model path governance
Audit-ready evidence by default
AWS-aligned architecture
Why Now
Enterprise AI is moving from pilots into real business workflows.
But trust controls are still fragmented across privacy, security, legal, compliance, data governance, and audit teams. DigiTrust gives enterprises a shared control layer to approve, enforce, monitor, and prove trusted AI use before sensitive data is exposed.
Purpose and consent drift
Map data usage to consent, lawful basis, purpose limitations, contractual restrictions, and internal policy requirements.
Audit readiness gaps
Capture policy decisions, transformations, approvals, model paths, user actions, and evidence packages for compliance review.
Trust Control Loop
Classify, decide, transform, approve, route, and prove every governed AI workflow.
1
Classify
Know what data is being used.
2
Decide
Check whether use is allowed.
3
Transform
Make data safer before AI use.
4
Approve
Route high-risk decisions to accountable humans.
5
Route
Send data only through approved workflows and model paths.
6
Prove
Capture evidence for every decision.
Why Existing Tools Are Not Enough
DigiTrust adds execution, control, and proof to the enterprise AI stack.
| Existing tool type | What it does | What DigiTrust adds |
|---|---|---|
| AI governance tools | Register models and assess risk | Controls AI workflow execution |
| GRC platforms | Track risk and compliance tasks | Produces workflow-level evidence |
| Privacy platforms | Manage notices, rights, and assessments | Enforces privacy controls in data and AI workflows |
| Data governance tools | Catalog and classify data | Governs how sensitive data is used in AI |
| Security tools | Detect threats and protect systems | Proves trusted AI use across business workflows |
What DigiTrust Proves
Every governed workflow can produce an evidence packet.
DigiTrust makes audit-ready evidence concrete by capturing the controls, decisions, approvals, and actions behind each governed AI workflow.
- Data classification result
- Policy decision
- Consent or purpose check
- Transformation applied
- Human approval record
- Model path used
- Enforcement action
- Timestamped audit trail
AWS Reference Architecture
Built for AWS-native trust, privacy, AI, and data governance.
DigiTrust can be deployed as an AWS-aligned control platform using managed services for policy, identity, AI workflows, sensitive data discovery, observability, and immutable evidence.
View the AWS Reference Architecture
Amazon Bedrock
Bedrock Agents
Bedrock Guardrails
Verified Permissions
AWS IAM
Lake Formation
Amazon Macie
AWS Glue
API Gateway
AWS Lambda
Step Functions
CloudTrail
AWS Config
Audit Manager
S3 Object Lock
AI Trust Readiness Score
Get your AI Trust Readiness Score.
DigiTrust evaluates your organization across the control areas required to make enterprise AI safe to scale.
Sensitive data exposure
Identify where protected, regulated, or confidential data may enter AI workflows.
Policy enforceability
Assess whether policies can be translated into repeatable controls and decisions.
Consent and purpose alignment
Evaluate whether data use matches consent, purpose, legal basis, and contractual limits.
AI model path governance
Review whether AI requests are routed through approved models, tools, and access paths.
Human approval readiness
Measure how high-risk or ambiguous workflows are escalated to accountable reviewers.
Evidence completeness
Determine whether every decision can be explained, reconstructed, and defended.
Audit defensibility
Assess whether evidence is reliable, timestamped, tamper-resistant, and review-ready.
AWS architecture maturity
Map current AWS services and gaps against trust infrastructure requirements.
Service Offerings
Start with readiness. Expand into managed DigiTrust operations.
1. DigiTrust Readiness Assessment
Assess AI trust risk, data exposure, AWS architecture readiness, policy maturity, and audit gaps.
2. Strategy and Reference Architecture
Define the target operating model, AWS service map, governance controls, architecture roadmap, and implementation plan.
3. DigiTrust MVP
Implement priority data flows, policy checks, classification, agent-assisted investigation, approvals, and audit evidence.
4. Platform Modules
Add AI use-case review, consent-purpose enforcement, runtime gateway controls, tokenization, and evidence dashboards.
5. Managed DigiTrust Operations
Operate monitoring, policy changes, exceptions, evidence requests, agent governance, and compliance reporting.
Trusted AI Assistance
AI assists. Trust controls stay in charge.
DigiTrust uses governed AI assistance to investigate, recommend, summarize, and generate evidence while approved policies, deterministic enforcement, and human approvals remain in control.
- Purpose-aware policy checks
- Consent and preference alignment
- AI model path governance
- Immutable audit trail
Next Step
Get Your AI Trust Readiness Score.
Use the form to request a 30–45 minute discussion focused on your AI trust risk, sensitive data exposure, AWS architecture, compliance evidence, and target operating model.
Contact info@digitranshq.com.