Screener/Databricks

Databricks

Unified data/AI platform with growing model governance capabilities via Unity Catalog and MLflow.

HQ🇺🇸 US
Est2013
Size5001-10000
databricks.com
Score
44.0 / 100
Evidence
6 items
Confidence
medium

Developing safety practices - core foundations in place with room for improvement.

Weaknesses:Governance Maturity, Technical Safety, Risk Assessment, Regulatory Readiness, External Engagement
Focus Areas
data platformMLOpsAI governanceUnity Catalog

Strengths

No notable strengths identified

Risks

  • Risk requires attention
Table of Contents

Security Assessment

Security-relevant indicators for vendor evaluation

Security Posture
42
TS-01dim: 45
Red Teaming & Pre-deployment Testing
Adversarial testing before deployment
TS-05dim: 45
Robustness & Adversarial Resilience
Resistance to adversarial attacks
RA-01dim: 38
Sector-Specific Risk Assessment
Risk analysis for deployment context
RA-03dim: 38
Dual-Use & Misuse Risk
Dangerous capability awareness
RA-07dim: 38
Incident History & Track Record
Past incidents and response quality
EE-04dim: 42
Vulnerability Disclosure Program
Bug bounty or CVE reporting process
Incident History
Databricks incident records sourced from AIAAIC Repository and public reporting.
Integration: AIAAIC, OECD AI Incidents Monitor
Third-Party Audits
External audit reports, SOC 2 attestations, and ISO certifications verified where published.
Sources: Company filings, registry lookups
CVE & Disclosures
Known vulnerabilities and security advisories from NVD, GitHub Security Advisories, and vendor pages.
Sources: NVD, GHSA, vendor disclosure pages

Dimension Breakdown

GM
Governance Maturitypreliminary
Published policies, corporate structure, safety mandate, whistleblowing, executive commitment.
48
TS
Technical Safetypreliminary
Benchmarks, adversarial robustness, fine-tuning safety, watermarking, model cards, research output.
45
RA
Risk Assessmentpreliminary
Dangerous capability evaluations, thresholds, external testing, bug bounty, halt conditions.
38
RR
Regulatory Readinesspreliminary
ISO 42001, EU AI Act compliance, GPAI obligations, international commitments, incident reporting.
45
EE
External Engagementpreliminary
Survey participation, research support, transparency, behavior specs, open-source contributions.
42

Social Impact & Safety Profile

Moderate

Databricks provides the Lakehouse platform used by many enterprises for AI/ML workloads. Unity Catalog offers data and model governance features. MLflow is a widely adopted open-source ML lifecycle tool. AI safety is addressed through enterprise data governance rather than direct model safety research. Strong enterprise security posture.

data governanceMLOpsmodel governanceenterprise security

Peer Comparison

Confident Security
C+47

Robustness & Adversarial

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Aim Security
C43.6

Robustness & Adversarial

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Noma Security
C41.1

Robustness & Adversarial

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Vendict
C40

Governance Tooling

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Data Sources & Methodology

Scoring methodology v0.1 · 40 indicators · 6 frameworks

Last assessment: 2026-03-23 · Confidence: medium · Evidence: 6 items

NIST AI RMF · EU AI Act · ISO 42001 · FLI AI Safety Index · MLCommons AILuminate · METR

How is this company scored? →Full methodologyView in Screener

Scores reflect publicly available information. A low score may indicate limited transparency rather than poor safety practices.