Stability AI

hybrid · United Kingdom , London · founded 2019

Employees: 51-200 · Stage: Series B · Funding: $264,000,000

36.3

moderate

default-balanced
composite score

Open-source AI company that created Stable Diffusion and related generative models. Develops proprietary and open-weight image, video, audio, and language models while also leveraging external model components and research.

Analyst summary

Stability AI's Stable Diffusion is widely deployed open-weights imagery, but the company has faced serious financial distress, CEO departure, active Getty Images litigation over training data, and offers no IP indemnification. The open weights remain valuable; the company itself is a much harder recommendation.

Treat the open-weights models as a community resource; do not treat the company as a viable enterprise vendor today.

Rating: avoid

Compliance posture

SOC 2 Type II	No / not disclosed
ISO 27001	No / not disclosed
ISO 42001 (AI management system)	No / not disclosed
FedRAMP authorized	No / not disclosed
GDPR compliant	Yes
CCPA compliant	No / not disclosed
HIPAA compliant	No / not disclosed
NIST AI RMF aligned	No / not disclosed
CSA STAR certified	No / not disclosed
EU AI Act classification	general_purpose

Data handling

Trains on user data	no
Outputs feed model improvement	no
Data retention period	30 days
Can delete user data on request	Yes (SLA 30 days)
Default data residency	US
Encryption at rest	Yes (AES-256)
Encryption in transit	Yes
DPA available	No / not disclosed
Public subprocessor list	No / not disclosed
HIPAA BAA available	No / not disclosed

IP profile

User owns outputs	yes
Vendor claims output rights	No / not disclosed
Input IP protection	moderate
Indemnification offered	No / not disclosed
Copyright shield program	No / not disclosed
Commercial use permitted	Yes
Training data provenance	partially_disclosed
Known IP lawsuits	2
Getty Images lawsuit (UK and US) for training on copyrighted images; artist class action (Andersen v. Stability AI)

Jurisdiction

Incorporation country	United Kingdom
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	Yes

Governance

Publishes model cards	Yes
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	No / not disclosed
Responsible-AI policy	https://stability.ai/use-policy
Terms of service	https://stability.ai/terms-of-use
Privacy policy	https://stability.ai/privacy-policy

Incidents on record

Date	Severity	Incident
2024-03-01	high	CEO Emad Mostaque resignation and financial instability reports [source]
2023-02-01	high	Getty Images copyright infringement lawsuit [source]

OWASP LLM Top 10 cross-walk

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	Prompt Injection User-supplied prompts manipulate model behaviour to bypass intended controls. SecurityTransparencyDependency chain
LLM02	Sensitive Information Disclosure Models leak PII, PHI, secrets, or proprietary data through outputs. Data handlingIP exposureJurisdiction
LLM03	Supply Chain Risk propagates from upstream models, datasets, plug-ins, and vendors. Dependency chainBusiness stabilitySecurity
LLM04	Data and Model Poisoning Adversarial training data or fine-tuning input degrades model integrity. Data handlingTransparencySecurity
LLM05	Improper Output Handling Downstream systems blindly trust model output, enabling injection downstream. IP exposureTransparency
LLM06	Excessive Agency Agents granted overbroad tool, identity, or permission scopes cause harm. Dependency chainTransparencyJurisdiction
LLM07	System Prompt Leakage System prompts containing secrets or logic are extracted via crafted input. Data handlingTransparency
LLM08	Vector and Embedding Weaknesses Vector stores and RAG pipelines leak or contaminate retrieved context. Data handlingSecurity
LLM09	Misinformation Hallucinated, biased, or fabricated outputs treated as authoritative. TransparencyRegulatory complianceBusiness stability
LLM10	Unbounded Consumption 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

NIST AI RMF cross-walk

How each NIST AI RMF function is supported by the dimensions TrustAtlas scores.

GOVERN	Govern Establish AI governance structure: policies, roles, accountability. Regulatory complianceJurisdictionTransparencyBusiness stability
MAP	Map Establish AI context: intended purpose, use cases, capabilities, and risks. TransparencyDependency chainData handlingIP exposure
MEASURE	Measure Quantitative + qualitative risk assessment: testing, benchmarks, monitoring. SecurityData handlingTransparency
MANAGE	Manage Treat identified risks: mitigation, controls, incident response, lifecycle. Regulatory complianceSecurityDependency chainBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/nist-ai-rmf

Cited sources

Field	Source
data_handling.trains_on_user_data	https://stability.ai/terms-of-service Verified 2026-04-19 by admin
governance.financial_stability	https://www.bloomberg.com/news/articles/2024-03-22/stability-ai-ceo-emad-mostaque-resigns Verified 2026-04-19 by admin
ip_profiles.indemnification_offered	https://stability.ai/terms-of-service Verified 2026-04-19 by admin
ip_profiles.known_ip_lawsuits	https://www.reuters.com/legal/litigation/getty-images-stability-ai-uk-trial-begins-2025-06-09/ Verified 2026-04-19 by admin
ip_profiles.training_data_provenance	https://stability.ai/news/stable-diffusion-public-release Verified 2026-04-19 by admin
jurisdiction_profiles.incorporation_country	https://stability.ai/about Verified 2026-04-19 by admin
security_compliance.gdpr_compliant	https://stability.ai/privacy-policy Verified 2026-04-19 by admin
security_compliance.soc2_type2	https://stability.ai/security Verified 2026-04-19 by admin

Questions to ask before signing

Vendor-agnostic baseline. Send these to the vendor and require written answers before contract.

01. Provide your most recent SOC 2 Type II report (with bridge letter if applicable).
02. Describe your training-data provenance and customer opt-out mechanics in writing.
03. List all sub-processors and confirm notification policy for material additions.
04. Confirm BAA availability and signed-BAA process if we process PHI.
05. Describe rate-limiting, quota, and circuit-breaker controls protecting our usage.
06. Provide your model card or equivalent disclosure documenting intended use, limitations, and known failure modes.
07. Describe your prompt-injection defences and red-team posture against OWASP LLM Top 10 risks.
08. Confirm data residency options and which sub-regions our data may touch.
09. Provide incident-response SLAs, security-event notification timelines, and the most recent pen-test report summary.
10. Confirm output ownership terms and any indemnification or copyright-shield programs available.
11. Describe acquisition-risk safeguards and what happens to our data on a change of control.
12. List foundation-model dependencies and how upstream-model risk is mitigated.

Methodology + caveats

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.