Where trusted AI matters most.
Mithra applies the same trust layer wherever AI touches regulated data or revenue-critical decisions. One architecture, every vertical.
Healthcare & Life Sciences
Clinical trials, regulatory submissions, drug discovery, real-world evidence, and diagnostics.
Financial Services & Fintech
Risk modeling, fraud prevention, payments, lending, and digital assets. Verified data and model lineage for AI decisions regulators can inspect.
Education & Higher Education
Trusted AI for K–12 and universities. Academic integrity, verified sources, and defensible institutional AI policy.
Research
Reproducible scholarship and scientific AI. Data provenance, source verification, and grant and publication integrity.
Federal & Government
Defensible AI for public-sector workflows. Audit-ready provenance and chain of custody aligned to FedRAMP and the EU AI Act.
Supply Chain
Verified data and provenance across multi-party ecosystems.
Tech & IP-Heavy Companies
Protect proprietary data, source code, and AI outputs from leakage.
Provable AI across discovery and submission.
AI is accelerating life sciences while breaking trust. Mithra restores it across six use cases, each with full data lineage and audit-ready context.
Ground truth for trial AI
Full lineage and audit-ready outputs for FDA / EMA. Protects multi-million-dollar trial outcomes.
Provable AI-driven discovery
Every prediction traceable to datasets and cryptographically verifiable. Prevents black-box risk.
The trust backbone for RWE
Traceability and drift detection unlock regulatory-grade real-world data AI.
Audit-ready medical writing
Source-linked AI text and verifiable claims. Reduces submission risk and approval delays.
Trusted precision medicine
Data integrity from source to insight. Validated AI-generated biomarker associations.
Trust layer for clinical AI
Proof-of-answer architecture. Explainable, traceable outputs clinicians actually trust.
Trusted AI for regulated finance.
81% of financial services firms are now adopting AI, and fintechs lead incumbents in advanced adoption by 47% to 30% (Cambridge Centre for Alternative Finance, 2026). Data quality is still the leading barrier, which is the gap Mithra closes.
Explainable, defensible models
Supervisors expect documented, explainable models under SR 11-7 and the EU AI Act. Full data lineage traces every model input and output back to a governed source.
Audit-ready detection
Fraud and AML decisions must hold up to regulators and auditors. Each flagged decision carries an immutable, timestamped record of the data behind it.
Grounded customer answers
Hallucinated guidance creates real liability. Outputs are validated against the SSOT® before they reach a customer, which prevents ungrounded answers.
Trusted AI that protects student data and integrity.
92% of UK students now use AI in some form (HEPI Student Generative AI Survey, 2025), while data security and privacy remains the top barrier to institutional adoption, cited by 56% of higher-education professionals (Ellucian, 2026).
Provenance over guesswork
78% of administrators fear AI harms academic integrity (Ellucian, 2024). Verified sources and traceable outputs show where an answer came from, supporting honest use.
Student records stay governed
FERPA-protected data flows into AI tools without controls. DRM and access controls prevent student data reaching ungoverned models.
Defensible institutional AI
Most institutions still lack comprehensive AI policy. Audit-ready records and data lineage make campus AI defensible to boards and regulators.
Data lineage for reproducible science.
Fabricated references in biomedical papers rose roughly 12-fold in two years. About one in 277 papers in early 2026 cited a source that does not exist (analysis of nearly 2.5 million papers, Columbia University, published in The Lancet, 2026).
No fabricated citations
AI tools invent plausible references that do not exist. Every AI-surfaced source is verified against trusted data before it enters the record.
A provable record of the work
Missing raw data and undocumented steps make results impossible to reproduce. Real-time lineage captures datasets, transformations, and the AI outputs they produced.
Origin trail on every input
Multi-source datasets lose their origin as they move. On-chain provenance keeps a verifiable trail for every input and result.
AI security and auditability for government.
Federal agencies reported 3,611 AI use cases in 2025, up nearly 70% year over year (OMB AI use case inventory, via the Center for Democracy & Technology, 2026). Yet more than 85% of high-impact use cases lack required risk-mitigation detail (Brookings, 2026).
Audit by default
Only 31% of OECD governments run post-deployment audits of AI (OECD, 2025). Mithra captures immutable, timestamped evidence for every decision.
Show how AI decided
Oversight bodies and citizens need to see how AI reached a result. Traceable outputs carry full data lineage back to the source.
Sensitive data stays permissioned
Government data must stay governed. DRM and access controls prevent leakage into external models.
Verified provenance across multi-party chains.
62% of traceability initiatives stall before full deployment, driven by data inconsistency and partial adoption (Gartner, 2024). A shared, verifiable source of truth is what makes traceability hold.
One verifiable source of truth
Data crosses many borders and intermediaries with no shared record. On-chain provenance gives every stakeholder a record they can verify.
Counterfeit protection at the SKU level
Counterfeits enter at the item level. Verifiable provenance records prove authenticity through the distribution chain.
Reporting that holds up
EUDR and CSRD require verifiable traceability. Audit-ready lineage supports regulatory submissions without manual reconstruction.
AI security that prevents IP leakage.
Across 858,440 data-loss events involving AI tools, source code was the single most-uploaded data type (Verizon Data Breach Investigations Report, 2026). Shadow AI featured in 20% of breaches last year, adding about $670,000 to each (IBM Cost of a Data Breach, 2025).
Proprietary code stays in-house
Developers paste proprietary code into public models. Samsung restricted ChatGPT in 2023 after exactly this. DRM and guardrails prevent proprietary data reaching ungoverned LLMs.
Visibility into AI activity
Most enterprise AI use is invisible to IT. Real-time activity tracking and agent monitoring surface what is actually running.
Origin on AI-generated code
AI-written code enters the codebase untagged and breaks traceability. Lineage records the origin and context of every AI output.
Bring the trust layer to your vertical.
See Mithra verify a real AI workflow against your kind of data, end to end.