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
Scale AI safely with policy controls, human approvals, and audit-ready evidence.
Why Now
AI is moving into real business workflows, but trust is still scattered across meetings, tickets, policies, and audits. DigiTrust gives teams an active operating layer to review, monitor, and prove trusted AI use before risk becomes exposure.
Detect and reduce exposure of PII, PHI, PCI, confidential business data, and restricted client data before it reaches AI or external systems.
Map data usage to consent, lawful basis, purpose limitations, contractual restrictions, and internal policy requirements.
Capture policy decisions, transformations, approvals, model paths, user actions, and evidence packages for compliance review.
Trust-to-Evidence Workflow
Know what data is being used.
Check whether use is allowed.
Make data safer before AI use.
Route high-risk decisions to accountable humans.
Send data only through approved workflows and model paths.
Capture evidence for every decision.
What DigiTrust Proves
DigiTrust makes audit-ready evidence concrete by capturing the controls, decisions, approvals, and actions behind each governed AI workflow.
Deployment
DigiTrust helps enterprises build trusted AI workflows across cloud environments, with AWS-aligned controls for policy, identity, sensitive data discovery, observability, and audit-ready evidence.
DigiTrust can align with AWS services for policy enforcement, identity, AI workflow orchestration, sensitive data discovery, monitoring, and evidence capture.
AI Trust Readiness Score
DigiTrust evaluates your organization across the control areas required to make enterprise AI safe to scale.
Identify where protected, regulated, or confidential data may enter AI workflows.
Assess whether policies can be translated into repeatable controls and decisions.
Evaluate whether data use matches consent, purpose, legal basis, and contractual limits.
Review whether AI requests are routed through approved models, tools, and access paths.
Measure how high-risk or ambiguous workflows are escalated to accountable reviewers.
Determine whether every decision can be explained, reconstructed, and defended.
Assess whether evidence is reliable, timestamped, tamper-resistant, and review-ready.
Map current AWS services and gaps against trust infrastructure requirements.
Service Offerings
Assess AI trust risk, data exposure, AWS architecture readiness, policy maturity, and audit gaps.
Define the target operating model, AWS service map, governance controls, architecture roadmap, and implementation plan.
Implement priority data flows, policy checks, classification, agent-assisted investigation, approvals, and audit evidence.
Add AI use-case review, consent-purpose enforcement, runtime gateway controls, tokenization, and evidence dashboards.
Operate monitoring, policy changes, exceptions, evidence requests, agent governance, and compliance reporting.
Trusted AI Assistance
DigiTrust uses governed AI assistance to investigate, recommend, summarize, and generate evidence while approved policies, deterministic enforcement, and human approvals remain in control.
Next Step
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.