AI Supply Chain Risk: Vendors, Subprocessors, and Model Provenance
Manage AI supply chain risk across model providers, hosting regions, fine-tuning partners, and embedded AI features in SaaS you already buy.
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Insights, guides, and latest trends from the world of AI tools
Latest Blogs
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Synthetic Data Governance for AI Tool Training and Testing
Govern synthetic data used with AI tools: generation methods, re-identification risk, labeling, retention, and when synthetic data still triggers privacy review.
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Shadow AI Discovery Program: Find Unapproved Tools Before Auditors Do
Detect shadow AI with network signals, expense audits, SSO gaps, and employee surveys, then route discoveries into your AI inventory without punishing reporters.
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EU AI Act Conformity Assessment for Deployed AI Tools
Navigate conformity assessment paths for high-risk AI tools: internal control, notified body involvement, technical documentation, and post-market monitoring.
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Model Card Review Process for Third-Party AI Tools
Evaluate vendor model cards before procurement: required fields, limitation claims, bias disclosures, and when model cards are insufficient for due diligence.
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Algorithmic Impact Assessment for AI Tool Deployments
Run algorithmic impact assessments before deploying AI tools: stakeholder mapping, harm scenarios, mitigation controls, and sign-off documentation.
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AI Tool Red Teaming Program: Scope, Cadence, and Evidence
Design a red teaming program for AI tools: attack surfaces, test cadence, severity scoring, remediation SLAs, and audit evidence regulators expect.
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Foundation Model Usage Policy for Enterprise AI Tools
Write a foundation model usage policy covering approved models, data classes, fine-tuning rules, GPAI provider obligations, and exception workflows.
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Internal Transparency Labeling for AI-Assisted Deliverables
Standard labels when work products used AI assistance—internal and external consistency.
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