Collibra
Overview
Product details compiled from public sources, each with a citation.
Matrix Coverage
Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.
| Asset class | Identify | Protect | Coverage | Source |
|---|---|---|---|---|
| AI Model | Protect: Not covered | Primary | 1 | |
| Training Data | Secondary | 2 |
Framework Relevance
These frameworks include controls relevant to the asset classes Collibra defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Collibra implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI Model | Models; Algorithms (model configuration) |
| Training Data | Training data | |
| CSA AI Controls Matrix | AI Model | Model Security; Governance, Risk and Compliance |
| Training Data | Data Security and Privacy Lifecycle Management; Model Security | |
| ISO 42001 | AI Model | A.6 AI system life cycle; A.10 Third-party and customer relationships; A.5 Assessing impacts of AI systems |
| Training Data | A.7 Data for AI systems | |
| Google SAIF | AI Model | Protect the AI model; ensure model integrity, provenance, and weight security |
| Training Data | Secure training data; data-security foundations; dataset provenance and integrity | |
| SANS Critical AI Security Guidelines | AI Model | Conventional Security Controls (protect model parameters with least privilege, encryption at rest, runtime obfuscation, and trusted execution environments); Data/Model Engineering Controls (adversarial training; alignment and fine-tuning); AI Supply Chain Management (public-model caution; transfer-attack exposure) |
| Training Data | Conventional Security Controls (defend training data; avoid data commingling); Data/Model Engineering Controls (data-quality controls; poison-robust training); Data Minimization and Obfuscation (differential privacy; synthetic data; federated learning) | |
| MITRE ATLAS | AI Model | AML.T0043 Craft Adversarial Data; AML.T0024 Exfiltration via AI Inference API (subtechniques: AML.T0024.001 Invert AI Model and AML.T0024.002 Extract AI Model); AML.T0018 Manipulate AI Model (integrity and backdoor) |
| Training Data | AML.T0020 Poison Training Data; AML.T0019 Publish Poisoned Datasets; AML.T0024.000 Infer Training Data Membership | |
| OWASP AI Exchange | AI Model | Development-time and runtime model threats: model inversion, extraction, evasion, poisoning |
| Training Data | Development-time threats: data poisoning, backdoor injection, dataset integrity violations | |
| OWASP LLM Top 10 | AI Model | LLM03 Supply Chain; LLM04 Data and Model Poisoning; LLM09 Misinformation |
| Training Data | LLM04 Data and Model Poisoning; LLM03 Supply Chain (dataset provenance) | |
| OWASP Agentic Security Top 10 | AI Model | ASI04 Agentic Supply Chain Vulnerabilities (model provenance, weights, and dynamic loading) |
| Training Data | ASI04 Agentic Supply Chain Vulnerabilities (dataset provenance and integrity) |
Provenance
Last sourced 2026-06-09.
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Sources
- Collibra AI Model Governance
- “a centralized capability to register, monitor and govern models across their lifecycle.”
- Collibra AI Governance
- “With templates for the EU AI Act, NIST AI RMF and custom assessments, teams can document risk levels, controls, performance metrics and required approvals.”
Changelog
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Refined product description wording.
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Enriched from the Collibra AI Governance documentation.
Found an error? Corrections are welcome. Suggest an edit.