Originally published on CoreProse KB-incidents
General counsel now must approve AI systems that affect millions of customers and vast data stores, while regulators, courts, and attackers already treat those systems as critical infrastructure.[2][5]
The risk is not “AI” itself but opaque decisioning, uncontrolled data flows, and unclear accountability layered onto existing duties and sector rules.[3][6]
This guide turns that concern into a concrete control plan you can drive with your CTO, CISO, and engineering leadership—without blanket bans.
The AI Risk Landscape: Why General Counsel Are Right to Worry
Regulators are moving from policy papers to enforcement:
EU AI Act and similar regimes enable fines in the tens of millions; the $1.16B Didi penalty shows opaque algorithms and data misuse are already punished at scale.[2]
In financial services, UK regulators will govern AI through existing conduct, disclosure, and prudential rules, not bespoke AI laws.[3][6][7]
Failures will be treated as mis‑selling, unfair treatment, or resilience gaps, not exotic “AI accidents.”[3]
💼 In practice: Supervisors are asking for “AI stress tests” that resemble model‑risk reviews, but now include generative models and explainability expectations.[6]
Fragmented but escalating regulatory environment
You must navigate overlapping layers:[2][3][6][8]
U.S. federal efforts (e.g., Executive Order) to coordinate AI policy
State‑level AI, privacy, and automated‑decision laws
International and sectoral regimes (financial services, health, employment)
⚠️ Warning: If an AI‑mediated decision harms someone, you will be judged under all applicable regimes, not just those that mention “AI.”[2][6]
Security incidents are already here
Recent AI‑related incidents (e.g., Anthropic, Mercor) show:[5]
Exposure often comes from integrations, storage, and dependencies, not the core model
Root causes: human error, misconfigured infrastructure, weak software‑supply‑chain controls[5]
📊 Key takeaway: Treat AI as part of your normal software and DevSecOps stack—because attackers already do.[5]
Courts, professional duties, and “AI‑assisted” work
Courts have sanctioned lawyers who used generative AI and submitted hallucinated citations.[9][11] Emerging norms:[9][11]
Duties of competence and supervision fully apply when AI is used
Professionals remain responsible for every word and decision, regardless of which assistant drafted it
The same logic will shape oversight expectations for brokers, clinicians, HR, and other regulated roles using AI.[3]
Your vendors’ AI is your risk surface
With ~78% of organizations using AI in at least one function, AI is already embedded in your supply chain.[12]
Risks:
“Shadow AI” in SaaS tools and productivity suites[12]
Vendor systems quietly shaping regulated decisions (credit, employment, pricing)
💡 Mini‑conclusion: The main risk is not pilots; it is production‑adjacent systems and vendor tools already influencing real decisions. Governance must start there.
Designing Accountable AI Architectures: Logs, Oversight, and the Three Lines of Defense
Core question for GCs: Can we quickly reconstruct why an AI‑mediated decision was made if challenged by a regulator, court, or customer?[1][2]
Build decision‑traceable agents
Production AI agents should emit an audit trail that captures decision lineage:[1]
Initial user input
Tool selections and external API calls
Intermediate reasoning (scores, policy lookups)
Retrieved context (documents, policies)
Final output or action
For a mortgage agent, logs should show application data, credit score retrieval, internal risk classification, policy consultation, and final approval or decline.[1]
Logs must be chronological and tamper‑evident to function like stack traces for legal and regulatory review.[1][2]
⚡ Engineering pattern: Use OpenTelemetry plus a structured event schema, tagging prompts, tool calls, and outputs with correlation IDs.[1]
Three Lines of Defense for AI
Adapt the existing Three Lines of Defense model to AI:[10]
First line – Business / product teams
Own AI use cases and risk assessments
Implement guardrails and human‑in‑the‑loop controls
Second line – Risk, compliance, privacy
Challenge risk assessments and controls
Define testing, thresholds, and escalation paths[10]
Third line – Internal audit
Audit algorithms and data governance
Validate adherence to policy and regulatory expectations[10]
💼 Example: For digital lending: first line documents model purpose and data; second line approves bias tests; third line samples approved/declined loans against logged decision lineage.[2][10]
Turn high‑level principles into engineering requirements
Government LLM guidance highlights five control areas—risk, privacy, transparency, human oversight, testing.[2] Translate into asks:[2][9][10]
Risk assessment: Model cards covering use, limits, prohibited inputs
Privacy: Mask sensitive data in prompts; encrypt logs in transit and at rest
Transparency: Notify users when AI is used; provide explanations for key decisions
Human oversight: Clear thresholds where human review or override is mandatory
Testing & validation: Bias tests, red‑teaming, regression tests before updates
⚠️ Non‑negotiable: AI is a drafting and triage tool, not an autonomous lawyer, banker, or clinician. It can summarize documents and flag patterns; it cannot replace professional judgment.[9][11]
💡 Mini‑conclusion: Without decision lineage and a mapped Three Lines of Defense, you have an unmanaged experiment—not an accountable AI system.
Security, Privacy, and Incident Readiness for AI Systems
AI security is about data and connectivity: how prompts, outputs, embeddings, and tool calls flow through your systems and vendors.[5]
Secure the AI stack, not just the model
The Anthropic and Mercor incidents highlight familiar patterns:[5]
Publicly accessible internal files and misconfigured storage
Release‑packaging errors that exposed code
Compromised open‑source dependencies connecting apps to AI services
Dependency scanning and SBOMs for AI components
Hardened CI/CD for model and agent releases
Strong access control for prompts, logs, and fine‑tuning data
⚡ Engineering ask: Treat LLM gateways, vector stores, and prompt logs as sensitive production systems, subject to full identity, patching, and change‑management controls.[5][10]
Use OWASP’s LLM checklist as your baseline
OWASP’s LLM AI Security & Governance Checklist targets executive tech, cybersecurity, privacy, compliance, and legal leaders.[4] It frames “trustworthy AI” as an assurance problem: are outputs factual, correct, and safe to apply?[4]
Threat‑model LLM‑specific abuse and prompt injection
Define abuse cases (fraud, harassment, data exfiltration) and monitoring rules
Implement privacy controls for training data, telemetry, and retention
📊 Practical move: Ask your CISO to map your top three AI applications against OWASP’s checklist and feed gaps into the risk register.[4]
Privacy and regulatory alignment
Government checklists stress:[2]
Encryption for sensitive data and per‑tenant keys
Role‑based access to prompts, logs, and decision trails
Clear retention and deletion rules for training and evaluation data
UK regulators’ technology‑neutral stance means AI remains subject to conduct, prudential, and operational‑resilience rules, including incident response and model‑risk governance.[6][7]
As EU AI Act duties phase in, incident playbooks must link technical detections (e.g., jailbreaks) to legal triage and required notifications across regimes.[2][8]
⚠️ Mini‑conclusion: If incident response does not mention prompts, model changes, or AI vendors, it is not prepared for your most likely failures.
Vendors, Contracts, and Cross‑Functional Guardrails
Because most organizations already rely heavily on third‑party AI, contracts may be your most effective control surface.[12]
Make vendor AI use visible
Given AI’s ubiquity across business functions, hidden AI inside SaaS and productivity tools is inevitable.[12] Contracts should require:[12]
Disclosure of where and how AI is used in delivering services
Notice when vendors add AI features or change model providers
Identification of any training use of your data
💡 Clause pattern: “Vendor must proactively disclose all use of AI systems that process Customer Data or materially influence services.”
Control data use and assign liability
Data‑use clauses should:[2][10][12]
Prohibit training third‑party models on your confidential data without explicit consent
Restrict cross‑tenant aggregation that could reveal sensitive patterns
Require deletion or de‑identification on termination
For high‑impact AI decisions, you can:
Mandate human oversight for specified outputs
Require documented bias and accuracy thresholds
Assign liability for erroneous or biased AI outputs, backed by indemnities and audit rights[12]
💼 M&A reality check: Overreliance on AI‑generated diligence summaries without robust human review can fuel post‑closing claims of misrepresentation or missed risks.[9]
Push oversight down the chain
Law‑firm AI checklists emphasize that AI‑assisted work must be verified as if produced by a junior.[9][11] Generalize this to key partners:[9][10][11][12]
Require outside counsel and advisors to maintain AI‑use policies
Specify that AI‑assisted work is fully subject to their professional standards
Reserve rights to ask about their AI controls when work is challenged
Sector‑agnostic playbooks propose seven core questions on purpose, data, monitoring, and auditability for high‑risk AI projects.[10] Use them as standardized due‑diligence questions for vendors and internal initiatives before regulators or reporters do.[10]
Sources & References (10)
1A Guide to Compliance and Governance for AI Agents Audit trails for AI agents are chronological records that document every step of an agent's decision-making process, from initial input to final action.
Consider a mortgage approval agent: the audit ...- 2Checklist for LLM Compliance in Government Deploying AI in government? Compliance isn’t optional. Missteps can lead to fines reaching $38.5M under global regulations like the EU AI Act - or worse, erode public trust. This checklist ensures you...
3Regulating Algorithmic Accountability in Financial Advising: Rethinking the SEC's AI Proposal — C Wang - Buffalo Law Review, 2025 - digitalcommons.law.buffalo.edu Author: Chen Wang
Abstract
As artificial intelligence increasingly reshapes financial advising, the SEC has proposed new rules requiring broker-dealers and investment advisers to eliminate or neutral...4OWASP's LLM AI Security & Governance Checklist: 13 action items for your team John P. Mello Jr., Freelance technology writer.
Artificial intelligence is developing at a dizzying pace. And if it's dizzying for people in the field, it's even more so for those outside it, especia...5Anthropic Leak and Mercor AI Attack: Takeaways for Enterprise AI Security Anthropic Leak and Mercor AI Attack: Takeaways for Enterprise AI Security
April 07, 2026 Jennifer Cheng
Recent AI security incidents, including the Anthropic leak and Mercor AI supply chain attack, ...- 6UK Financial Services Regulators’ Approach to Artificial Intelligence in 2026 | Global Policy Watch Artificial intelligence (“AI”) continues to reshape the UK financial services landscape in 2026, with consumers increasingly relying on AI-driven tools for financial guidance and firms deploying more ...
- 7UK Financial Services Regulators’ Approach to Artificial Intelligence in 2026 | Inside Global Tech Artificial intelligence (“AI”) continues to reshape the UK financial services landscape in 2026, with consumers increasingly relying on AI-driven tools for financial guidance and firms deploying more ...
82026 AI Laws Update: Key Regulations and Practical Guidance Gunderson Dettmer
European Union, USA February 5 2026
This client alert provides a high-level overview of key AI laws enacted or taking effect in 2026. With President Trump’s December 2025 Executive...- 9AI’s Due Diligence Applications Need Rigorous Human Oversight Artificial intelligence is becoming a value-enhancing tool in private equity transactions. Mergers and acquisitions require meticulous due diligence to assess opportunities, risk, and compliance. The ...
- 10Compliance Checklist for AI and Machine Learning AI is no longer "some science-fiction side of technology – it is normal computer programming now,” Eduardo Ustaran of Hogan Lovells told the Cybersecurity Law Report, and efforts to regulate AI and ma...
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