AI pillar tips

Operational playbook for responsible AI deployment

These practitioner tips align Zeph Tech research with regulatory expectations from the EU AI Act, U.S. agency guidance, and international management system standards.

Use them to evaluate vendors, design internal controls, and brief governance forums before scaling automation.

Governance and accountability

  • Create a system inventory covering training data, model lineage, release status, and human oversight responsibilities as required by the EU AI Act’s Article 60 documentation obligations.
  • Map each use case to NIST AI RMF risk profiles, documenting impact assessments, foreseeable misuse, and mitigation plans before production approval.
  • Integrate MAS Veritas Toolkit controls by assigning fairness testing owners, recording explainability evidence, and logging board attestations aligned to the 2025 control catalogue (PDF).
  • Map treaty obligations from the Council of Europe AI Convention and UNESCO’s ethics implementation report into governance charters so cross-border deployments account for human-rights impact assessments and transparency duties.
  • Implement management reviews that satisfy ISO/IEC 42001:2023 clauses 9.2 and 9.3—ensure leadership evaluates monitoring results, nonconformities, and continual improvement items each quarter.
  • Operationalise OMB M-24-10 requirements by appointing a chief AI officer, gating high-risk launches behind AI governance board approvals, and publishing impact assessment summaries per the official memorandum (PDF).

Data stewardship and privacy

  • Enforce data minimisation using ISO/IEC 27701 controls and GDPR Article 5 requirements—document why every dataset attribute is necessary for the stated purpose.
  • Track consent signals and contractual bases for training data, ensuring opt-out mechanisms mirror obligations in state privacy laws such as Colorado and California consumer rights.
  • Retain dataset provenance packages including collection source, license, and curation steps; regulators increasingly request these files during investigations and certification audits.
  • Validate protected class coverage by running fairness tests aligned to EEOC and Department of Labor guidance when models influence employment, lending, or housing decisions.

Model operations and monitoring

  • Instrument full audit trails across prompts, responses, and admin overrides; FTC enforcement actions frequently cite inadequate logging during investigations.
  • Deploy red-teaming workflows following NIST SP 800-204E, the NIST AI 600-1 draft profile, and CISA/UK AISI guidance—capture jailbreak results and remediation timelines in issue trackers.
  • Use model cards and system cards for every release, updating performance metrics, known limitations, and safe-use instructions as required by ISO/IEC 42001 clause 8.3.
  • Monitor post-deployment drift with statistical quality control, documenting thresholds that trigger retraining or rollback to satisfy EU AI Act Article 17 monitoring duties.

Workforce enablement

  • Deliver role-based training that distinguishes builder, reviewer, and end-user obligations—tie completion to HR systems to evidence compliance.
  • Provide contestability channels that mirror Department of Labor worker-well-being principles: workers must know when AI influenced outcomes and how to appeal.
  • Publish acceptable use standards covering prohibited prompts, high-risk scenarios, and data handling; align them with SOC 2 CC6/CC7 controls and customer contracts.
  • Review vendor obligations quarterly, ensuring partners refresh SOC 2 reports, ISO certifications, and incident notifications before renewals.