Risk Management for Law Firms: Engagement Letters, Policies, and Insurance
Law firms using AI need clear engagement letters, internal policies, and risk controls to manage liability.
Plain-English guides on AI risk and readiness for healthcare, HR, small business, finance, education, and law. Each post connects to help you understand the full picture of AI compliance and liability.
Law firms using AI need clear engagement letters, internal policies, and risk controls to manage liability.
Lawyers must protect client confidentiality. Using public AI tools without safeguards may risk privilege and privacy.
AI can speed up document review, but mishandling may cause privilege waivers or accidental disclosures.
AI drafts contracts quickly, but mismatched definitions, errors, and cross-reference gaps create hidden dangers.
AI can accelerate research, but fake citations and confidentiality risks raise malpractice concerns.
AI outages can halt operations, disrupt payroll, and frustrate customers. Planning ahead is essential.
Free AI tools may expose data, create compliance risks, and lock you into vendors later.
AI empowers small businesses but also raises risks from compliance gaps to insurance coverage holes.
Prompt injection is like phishing for AI systems — tricking tools into revealing what they shouldn’t.
AI chatbots can boost customer service, but they may also expose sensitive customer data if not managed carefully.
The EU's AI Act classifies employment AI as high risk, creating strict compliance demands for employers.
Workplace AI creates overlap between employment practices and cyber liability policies — often with gaps.
Offensive or biased AI outputs can still trigger harassment claims under employment law.
AI monitoring tools raise privacy, discrimination, and labor law challenges in the workplace.
AI hiring now spans assessments and onboarding — with liability risks at each stage.
AI in education may unintentionally expose student records, risking FERPA noncompliance.
Financial AI systems face scrutiny for bias, errors, and unclear auditability under strict regulations.
Key questions to clarify whether your insurance policy covers AI-related risks.
Healthcare startups adopting AI need to check whether HIPAA applies — and where gaps exist.
The EEOC warns that bias in AI hiring tools is still the employer's responsibility.
Cyber liability insurance may not fully address AI risks — coverage depends on fine print.
Plain-English explainer on how overconfidence in AI self-checks creates negligence liability and litigation risk for businesses.
How overconfidence reduces investment in audits and stress tests, creating brittle AI systems vulnerable to catastrophic failures.
How early AI cost savings get wiped out by litigation, remediation, and lost trust when self-oversight fails.
How customers lose trust when AI safeguards are revealed as self-policing theater. Plain-English guide to reputation risks.
How industries that over-promise AI safety today face harsher regulation later. Plain-English guide to regulatory backlash patterns.
Essential checklist for evaluating AI vendors: data handling, liability terms, audit rights, and contract red flags that protect your business.
Step-by-step crisis response plan for AI incidents: immediate containment, stakeholder communication, legal protection, and damage control.
How AI tools in real estate create fair housing risks, MLS compliance issues, and liability exposure for agents, brokers, and PropTech companies.
How AI models lose accuracy over time through data drift, concept drift, and distribution shifts — and why most businesses don't notice until it's too late.
How AI-powered telehealth platforms navigate HIPAA compliance: remote consultations, AI scribes, diagnostic support, and vendor responsibilities.
How regulators and insurers view AI diagnostic errors: malpractice liability, vendor responsibility, FDA oversight, and patient safety standards.
Essential questions for healthcare administrators evaluating AI vendors: PHI handling, HIPAA compliance, security controls, and liability protection.
How AI vendor contracts use subtle language to shift liability, limit coverage, and leave customers exposed. Plain-English guide to contract red flags.
How courts evaluate AI evidence: discovery obligations, log requirements, model validation, and what judges need to see when AI decisions are challenged.
Real-world liability scenarios for consultants, agencies, and professionals using AI in client work. Professional liability, malpractice, and contract risks explained.
How AI performance evaluation systems can create discrimination claims. Legal risks, bias detection, and compliance strategies for HR teams using AI reviews.
When AI employee monitoring crosses into illegal territory. Privacy laws, consent requirements, and compliance strategies for workplace AI surveillance.
Step-by-step practical guide for HR managers to audit AI hiring tools for bias and compliance. No technical background required.
How AI creates new SEC disclosure obligations. Practical guidance on material risk reporting, investor communications, and compliance liability for public companies.
How CFOs should evaluate AI as a material business risk. Practical framework for risk assessment, financial impact analysis, and disclosure decisions.
How AI in tax preparation creates new audit risks and compliance obligations. Practical guidance for tax professionals on IRS expectations, documentation, and liability.
Small business AI liability myths busted. What cyber insurance actually covers for AI incidents, coverage gaps, and practical risk management for small teams.
Create practical AI policies for small teams. Templates and plain-language guidance for AI use policies, data protection, and staff training that actually work.
Real stories of AI chatbot failures and how to prevent them. Practical guide for small businesses using customer service bots to avoid reputation damage and liability.