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US AI compliance

20/May/2026
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Agentic AI for Enterprise: Architecture & Governance

Agentic AI for enterprise works when three layers run together: architecture patterns, agent boundaries, and governance. See how to deploy…
04/May/2026
json [ { "question": "Why does AI governance need industry-specific overlays?", "answer": "Industry-specific AI governance overlays exist because regulated sectors impose controls a generic framework does not cover. Banking adds model risk and fair-lending rules. Healthcare adds PHI boundaries. Gaming adds responsible gambling triggers." }, { "question": "What does AI governance look like in BFSI?", "answer": "BFSI AI governance follows US SR 11-7 model risk management, OCC 2013-29 / 2023-17, Reg B and ECOA fair lending, FCRA adverse-action accuracy, AML and OFAC screening, and SOX auditability. NAIC Model AI Bulletin and NY DFS Circular Letter No. 7 add insurer and state-level expectations." }, { "question": "What does AI governance look like in healthcare?", "answer": "Healthcare AI governance starts with HIPAA Privacy, Security, and Breach Notification rules, HITECH, HITRUST CSF, 42 CFR Part 2 for substance-use records, and FDA SaMD guidance with Predetermined Change Control Plans for adaptive models. State privacy laws add CMIA, NY SHIELD, and CCPA / CPRA health-data rules." }, { "question": "What does AI governance look like in casino gaming and hospitality?", "answer": "Casino AI governance addresses Title 31 BSA reporting, FinCEN MSB obligations, and state gaming commission rules from Nevada GCB, NJ DGE, Pennsylvania PGCB, and Michigan MGCB. The American Gaming Association responsible gambling framework guides intervention thresholds and guest data isolation across player analytics, AML, and loyalty systems." }, { "question": "What belongs in every overlay regardless of industry?", "answer": "Every overlay needs three elements: a named regulator mapped to specific controls, a sector-specific incident reporting cadence, and domain-trained model evaluation criteria. Without those three, the overlay is a label, not a control." } ] illustration json [ { "question": "Why does AI governance need industry-specific overlays?", "answer": "Industry-specific AI governance overlays exist because regulated sectors impose controls a generic framework does not cover. Banking adds model risk and fair-lending rules. Healthcare adds PHI boundaries. Gaming adds responsible gambling triggers." }, { "question": "What does AI governance look like in BFSI?", "answer": "BFSI AI governance follows US SR 11-7 model risk management, OCC 2013-29 / 2023-17, Reg B and ECOA fair lending, FCRA adverse-action accuracy, AML and OFAC screening, and SOX auditability. NAIC Model AI Bulletin and NY DFS Circular Letter No. 7 add insurer and state-level expectations." }, { "question": "What does AI governance look like in healthcare?", "answer": "Healthcare AI governance starts with HIPAA Privacy, Security, and Breach Notification rules, HITECH, HITRUST CSF, 42 CFR Part 2 for substance-use records, and FDA SaMD guidance with Predetermined Change Control Plans for adaptive models. State privacy laws add CMIA, NY SHIELD, and CCPA / CPRA health-data rules." }, { "question": "What does AI governance look like in casino gaming and hospitality?", "answer": "Casino AI governance addresses Title 31 BSA reporting, FinCEN MSB obligations, and state gaming commission rules from Nevada GCB, NJ DGE, Pennsylvania PGCB, and Michigan MGCB. The American Gaming Association responsible gambling framework guides intervention thresholds and guest data isolation across player analytics, AML, and loyalty systems." }, { "question": "What belongs in every overlay regardless of industry?", "answer": "Every overlay needs three elements: a named regulator mapped to specific controls, a sector-specific incident reporting cadence, and domain-trained model evaluation criteria. Without those three, the overlay is a label, not a control." } ] illustration

Industry-Specific AI Governance: BFSI, Healthcare, Gaming

Industry-specific AI governance layers BFSI, healthcare, and gaming controls on a generic base. See what each sector adds, US-led with…
04/May/2026
json [ { "question": "What does auditing agentic AI in production require?", "answer": "Auditing agentic AI requires three layers built into the system from day one: scoped permission boundaries per agent, structured logs of every tool call and decision, and a rehearsed incident response playbook for autonomous failures. Without all three, agent behavior is effectively untraceable." }, { "question": "What should an AI agent permission boundary cover?", "answer": "An AI agent permission boundary covers data scopes, a tool and API whitelist, rate limits, maximum action cost per task, and the user context the agent inherits when acting on someone's behalf." }, { "question": "What belongs in an AI agent audit log?", "answer": "An AI agent audit log captures every prompt, tool call, retrieval, decision, confidence score, and human escalation trigger, with timestamps, agent identity, and a tamper-evident hash chain so events cannot be silently rewritten." }, { "question": "How do you respond to an autonomous agent incident?", "answer": "Respond in four steps: contain with a per-agent kill switch, roll back reversible actions, run root-cause analysis through the audit logs, and file regulatory reports where the failure crosses a reporting threshold." }, { "question": "Which regulations shape agent auditability?", "answer": "Agent auditability is shaped by the NIST AI RMF Manage function, SR 11-7 model risk oversight, SOX, HIPAA, Title 31 BSA and FinCEN, the NAIC Model AI Bulletin, and state laws including the Colorado AI Act and NY DFS Part 500." } ] illustration json [ { "question": "What does auditing agentic AI in production require?", "answer": "Auditing agentic AI requires three layers built into the system from day one: scoped permission boundaries per agent, structured logs of every tool call and decision, and a rehearsed incident response playbook for autonomous failures. Without all three, agent behavior is effectively untraceable." }, { "question": "What should an AI agent permission boundary cover?", "answer": "An AI agent permission boundary covers data scopes, a tool and API whitelist, rate limits, maximum action cost per task, and the user context the agent inherits when acting on someone's behalf." }, { "question": "What belongs in an AI agent audit log?", "answer": "An AI agent audit log captures every prompt, tool call, retrieval, decision, confidence score, and human escalation trigger, with timestamps, agent identity, and a tamper-evident hash chain so events cannot be silently rewritten." }, { "question": "How do you respond to an autonomous agent incident?", "answer": "Respond in four steps: contain with a per-agent kill switch, roll back reversible actions, run root-cause analysis through the audit logs, and file regulatory reports where the failure crosses a reporting threshold." }, { "question": "Which regulations shape agent auditability?", "answer": "Agent auditability is shaped by the NIST AI RMF Manage function, SR 11-7 model risk oversight, SOX, HIPAA, Title 31 BSA and FinCEN, the NAIC Model AI Bulletin, and state laws including the Colorado AI Act and NY DFS Part 500." } ] illustration

Auditing Agentic AI: Boundaries, Logs, Incident Response

Auditing agentic AI requires permission boundaries per agent, structured tool-call logs, and a rehearsed incident response playbook. Here is each…
04/May/2026
json [ { "question": "What is human-in-the-loop in AI governance?", "answer": "Human-in-the-loop AI governance is a control that routes low-confidence model outputs to a person for review before a decision takes effect, with logs, time-on-task data, and approval-rate monitoring." }, { "question": "Why does automation bias defeat human oversight?", "answer": "Automation bias is the tendency to trust a polished machine output more than the reviewer's own judgment, which pushes approval rates toward 100 percent and erases the value of the review." }, { "question": "How do US frameworks address automation bias?", "answer": "US frameworks address automation bias by expecting documented, meaningful human review on high-stakes AI decisions, with NIST AI RMF, FCRA, NAIC, and state laws as the lead references." }, { "question": "What review architecture prevents rubber-stamp approval?", "answer": "Review architecture prevents rubber-stamp approval through five design patterns: friction by design, minimum review time, approval-rate health metrics, structured justification, and escalation paths for edge cases." }, { "question": "How do confidence thresholds decide what routes to a human?", "answer": "Confidence thresholds set a score below which an AI output routes to human review, calibrated per risk tier so high-stakes decisions get tighter thresholds and lower automation rates." } ] illustration json [ { "question": "What is human-in-the-loop in AI governance?", "answer": "Human-in-the-loop AI governance is a control that routes low-confidence model outputs to a person for review before a decision takes effect, with logs, time-on-task data, and approval-rate monitoring." }, { "question": "Why does automation bias defeat human oversight?", "answer": "Automation bias is the tendency to trust a polished machine output more than the reviewer's own judgment, which pushes approval rates toward 100 percent and erases the value of the review." }, { "question": "How do US frameworks address automation bias?", "answer": "US frameworks address automation bias by expecting documented, meaningful human review on high-stakes AI decisions, with NIST AI RMF, FCRA, NAIC, and state laws as the lead references." }, { "question": "What review architecture prevents rubber-stamp approval?", "answer": "Review architecture prevents rubber-stamp approval through five design patterns: friction by design, minimum review time, approval-rate health metrics, structured justification, and escalation paths for edge cases." }, { "question": "How do confidence thresholds decide what routes to a human?", "answer": "Confidence thresholds set a score below which an AI output routes to human review, calibrated per risk tier so high-stakes decisions get tighter thresholds and lower automation rates." } ] illustration

Human-in-the-Loop AI Governance: Beyond Rubber Stamps

Human-in-the-loop AI governance fails when reviewers rubber-stamp outputs. Here is the review architecture that makes oversight meaningful under US rules.
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