Precision-medicine technology company applying AI to one of the world's largest libraries of clinical and molecular data. Provides genomic sequencing, clinical-trial matching (Deep 6 AI), and an AI-driven clinical decision platform. NASDAQ:TEM (IPO June 2024 at $37). FY2024 revenue ~$693M, 30%+ YoY growth.
| SOC 2 Type II | Yes (report 2025-12-01) |
| ISO 27001 | Yes |
| ISO 42001 (AI management system) | No / not disclosed |
| FedRAMP authorized | No / not disclosed |
| GDPR compliant | Yes |
| CCPA compliant | Yes |
| HIPAA compliant | Yes |
| NIST AI RMF aligned | No / not disclosed |
| CSA STAR certified | No / not disclosed |
| EU AI Act classification | high_risk |
| Trains on user data | varies_by_product |
| Outputs feed model improvement | opt_out_available |
| Data retention period | Per HIPAA retention requirements and contractual terms |
| Can delete user data on request | Yes (SLA 60 days) |
| Default data residency | US |
| Encryption at rest | Yes (AES-256 / TLS 1.2+) |
| Encryption in transit | Yes |
| DPA available | Yes |
| Public subprocessor list | No / not disclosed |
| HIPAA BAA available | Yes |
| User owns outputs | yes_with_caveats |
| Vendor claims output rights | No / not disclosed |
| Input IP protection | strong |
| Indemnification offered | Yes |
| Copyright shield program | No / not disclosed |
| Commercial use permitted | Yes |
| Training data provenance | mixed_sources |
| Known IP lawsuits | No / not disclosed |
| Incorporation country | US |
| Incorporation jurisdiction risk | low |
| Subject to US jurisdiction | Yes |
| Subject to EU jurisdiction | Yes |
| Subject to China jurisdiction | No / not disclosed |
| Subject to Russia jurisdiction | No / not disclosed |
| Government data access risk | moderate |
| Five Eyes aligned | Yes |
| Adequate privacy jurisdiction | No / not disclosed |
| Publishes model cards | No / not disclosed |
| Publishes transparency reports | No / not disclosed |
| Has AI ethics board | No / not disclosed |
| Safety testing disclosed | Yes |
| Red-teaming program | No / not disclosed |
| Government contracts | Yes |
| Terms of service | https://www.tempus.com/terms-of-service/ |
| Privacy policy | https://www.tempus.com/privacy-policy/ |
No incidents on file.
TrustAtlas dimensions that materially address each OWASP risk. Use to translate this vendor's compliance posture and data-handling stance into the application-security vocabulary your security team already uses.
| LLM01 |
User-supplied prompts manipulate model behaviour to bypass intended controls.
SecurityTransparencyDependency chain
|
| LLM02 |
Models leak PII, PHI, secrets, or proprietary data through outputs.
Data handlingIP exposureJurisdiction
|
| LLM03 |
Risk propagates from upstream models, datasets, plug-ins, and vendors.
Dependency chainBusiness stabilitySecurity
|
| LLM04 |
Adversarial training data or fine-tuning input degrades model integrity.
Data handlingTransparencySecurity
|
| LLM05 |
Downstream systems blindly trust model output, enabling injection downstream.
IP exposureTransparency
|
| LLM06 |
Agents granted overbroad tool, identity, or permission scopes cause harm.
Dependency chainTransparencyJurisdiction
|
| LLM07 |
System prompts containing secrets or logic are extracted via crafted input.
Data handlingTransparency
|
| LLM08 |
Vector stores and RAG pipelines leak or contaminate retrieved context.
Data handlingSecurity
|
| LLM09 |
Hallucinated, biased, or fabricated outputs treated as authoritative.
TransparencyRegulatory complianceBusiness stability
|
| LLM10 |
Cost, denial-of-service, and resource-exhaustion attacks against LLM endpoints.
SecurityBusiness stability
|
Full framework reference: https://trustatlas.pages.dev/framework/owasp-llm-top-10
How each NIST AI RMF function is supported by the dimensions TrustAtlas scores.
| GOVERN |
Establish AI governance structure: policies, roles, accountability.
Regulatory complianceJurisdictionTransparencyBusiness stability
|
| MAP |
Establish AI context: intended purpose, use cases, capabilities, and risks.
TransparencyDependency chainData handlingIP exposure
|
| MEASURE |
Quantitative + qualitative risk assessment: testing, benchmarks, monitoring.
SecurityData handlingTransparency
|
| MANAGE |
Treat identified risks: mitigation, controls, incident response, lifecycle.
Regulatory complianceSecurityDependency chainBusiness stability
|
Full framework reference: https://trustatlas.pages.dev/framework/nist-ai-rmf
| Field | Source |
|---|---|
| governance.government_contracts | https://finance.yahoo.com/quote/TEM/ Verified 2026-05-15 by analyst |
| ip_profile.training_data_provenance | https://www.tempus.com/terms-of-service/ Verified 2026-05-15 by analyst |
| security_compliance.hipaa_compliant | https://www.tempus.com/privacy-policy/ Verified 2026-05-15 by analyst |
| vendors.funding_stage | https://www.nasdaq.com/market-activity/ipos/overview?dealId=1033342-110202 Verified 2024-06-14 by analyst |
| vendors.last_valuation_usd | https://www.tradingview.com/symbols/NASDAQ-TEM/ Verified 2026-05-15 by analyst |
| vendors.public_ticker | https://www.tempus.com/news/pr/tempus-announces-pricing-of-initial-public-offering/ Verified 2024-06-13 by analyst |
Vendor-agnostic baseline. Send these to the vendor and require written answers before contract.
Composite scores use the default-balanced weight profile (25% data handling, 20% IP exposure, 15% jurisdiction, 15% security, 10% regulatory compliance, 8% transparency, 5% business stability, 2% dependency chain). All facts are sourced from the vendor's own public disclosures, public regulatory filings, or reputable secondary reporting — see the cited sources table above. This pack is decision-support material, not legal advice or audit evidence.