Company Overview
Data-centric AI platform that automatically detects and fixes data quality issues in ML datasets and LLM outputs.
Based in Silicon Valley (San Francisco, CA), Cleanlab offers its Cleanlab Studio as a solution for organizations navigating the complexities of protecting sensitive data across the AI lifecycle while enabling responsible innovation. The platform is positioned within the broader AI Data Security category, where AI Security Intelligence tracks 43 companies building specialized capabilities.
Founded in 2021, Cleanlab has been building its platform during the critical period when enterprise AI adoption — and the corresponding security challenges — began their exponential acceleration.
Why Watch This Company
In a market where data is simultaneously AI's greatest asset and its most significant liability, Cleanlab offers a focused approach to protecting sensitive data across the AI lifecycle while enabling responsible innovation. As regulatory requirements tighten and enterprise AI deployments scale, solutions that can protect data without degrading model performance will command significant market attention.
Key Facts
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Headquarters
San Francisco, CA
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Category
AI Data Security
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Key Product
Cleanlab Studio
Primary Product
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Cleanlab Studio
Data-centric AI platform that automatically detects and fixes data quality issues in ML datasets and LLM outputs.
AI Data Security Landscape
AI Data Security →
AI Data Security addresses one of the most fundamental challenges in enterprise AI adoption: protecting the data that trains, tunes, and powers AI systems without crippling their utility. This category spans the full data lifecycle — from training data curation and privacy-preserving computation to real-time data loss prevention for AI interactions and post-deployment data governance.
43 companies tracked in this category
Buyer's Evaluation Framework
Key questions to evaluate any AI Data Security vendor — including Cleanlab:
Can the platform discover and classify sensitive data flowing into AI training pipelines and inference endpoints?
Does the solution support privacy-enhancing technologies like differential privacy, federated learning, or homomorphic encryption?
How does the vendor prevent AI-specific data leakage scenarios such as model memorization, training data extraction, or prompt-based exfiltration?
Is the platform compatible with major cloud AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI) and self-hosted model deployments?
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Category Peers — AI Data Security
42 other companies in this category
Apheris
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Baffle
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BigID
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Cosmian
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Cyera
New York, NY
★ Featured Profile
Dasera
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Duality Technologies
Hoboken, NJ
Enveil
Washington, DC
Ethyca
New York, NY
Eureka Security
Tel Aviv, Israel
Forcepoint
Austin, TX
Granica
Seattle, WA
Gretel AI
San Diego, CA
Hazy
London, UK
Immuta
Boston, MA
Inpher
New York, NY
IronCore Labs
Denver, CO
MineOS
Tel Aviv, Israel
Mostly AI
Vienna, Austria
Nightfall AI
San Francisco, CA
Normalyze
Los Altos, CA
Opaque Systems
Berkeley, CA
Open Raven
Los Angeles, CA
Opsin AI
New York, NY
Privacera
Fremont, CA
Private AI
Toronto, Canada
Protecto
Palo Alto, CA
Relyance AI
Mountain View, CA
Sealpath
Madrid, Spain
Securiti AI
San Jose, CA
★ Featured Profile
Sentra
Tel Aviv, Israel
Sherpa.ai
Bilbao, Spain
Skyflow
Palo Alto, CA
Snorkel AI
Redwood City, CA
Symmetry Systems
San Francisco, CA
Theom
Sunnyvale, CA
Tonic AI
San Francisco, CA
TrustLogix
Palo Alto, CA
Tumult Labs
Cambridge, MA
Varonis
New York, NY
★ Featured Profile
Virtru
Washington, DC
Zama
Paris, France