AI Security Blueprint - Insurance Edition
Enterprise security architecture for insurance AI addressing policyholder data protection, GLBA compliance, NYDFS Cybersecurity Regulation, model theft prevention, adversarial robustness, and fraud detection AI security. Includes 50 security controls across data protection, model security, infrastructure, access control, and monitoring phases.
Key Insights
Insurance AI security faces unique challenges: sensitive policyholder data (PHI for health, financial data for life, claims details), distributed access across agents, brokers, TPAs, reinsurers, and regulators, legacy systems that predate modern security architectures, heavy reliance on third-party AI, and state-specific regulatory requirements including GLBA Safeguards Rule and NYDFS 23 NYCRR 500.
This blueprint provides insurance-specific security architecture for AI systems. It addresses insurance attack vectors (pricing algorithm theft, claims fraud manipulation, telematics poisoning) and insurance compliance requirements.
Overview
Insurance AI security isn't just about protecting models—it's about protecting policyholder data, preventing claims fraud manipulation, securing proprietary pricing algorithms, and meeting state regulatory requirements. Generic AI security guidance misses insurance-specific concerns.
This blueprint adapts comprehensive AI security architecture for insurance environments. It addresses the threats insurers face and the compliance requirements they must meet.
What's Inside
Insurance AI Threat Landscape
- Model theft: Competitors stealing pricing algorithms, underwriting rules, claims triage models
- Data poisoning: Fraudulent claims designed to corrupt fraud detection AI, telematics manipulation
- Policyholder data exposure: PHI, financial data, claims details
- Distributed access risks: Agent/broker/TPA access patterns
Security Architecture for Insurance AI
- Policyholder data protection controls
- Agent/broker access management
- TPA and reinsurer data sharing security
- Legacy system integration security
Regulatory Compliance Controls
- GLBA Safeguards Rule requirements for AI
- NYDFS 23 NYCRR 500 compliance
- State breach notification requirements
- Examination readiness
Third-Party AI Vendor Security
- Security assessment for ISO, Verisk, LexisNexis
- Contract security requirements
- Data handling verification
- Ongoing monitoring
Claims Fraud AI Protection
- Fraud detection model security
- Training data integrity controls
- Adversarial attack prevention
- Model monitoring
Actuarial Model Security
- Pricing model protection
- Access controls for sensitive algorithms
- Model theft prevention
- Intellectual property protection
Incident Response for Insurance
- Insurance-specific incident classification
- Regulatory notification requirements by state
- Policyholder notification procedures
- Commissioner communication
Who This Is For
- CISOs in insurance companies
- Chief AI Officers ensuring security governance
- Security Teams implementing AI security
- Compliance Officers meeting security regulations
- IT Leaders securing AI infrastructure
Why This Resource
Generic AI security blueprints don't address policyholder data requirements, state regulatory compliance, or the distributed access patterns unique to insurance. This blueprint speaks to insurance security concerns—making implementation relevant and compliance-focused.
Regulatory mapping ensures security controls satisfy GLBA and NYDFS requirements.
FAQ
Q: How do we secure AI when agents and brokers need access?
A: The distributed access section provides architecture for appropriate access controls—giving agents what they need while protecting sensitive data and preventing misuse.
Q: What about third-party AI we can't fully control?
A: The vendor security section provides assessment criteria, contract requirements, and monitoring approaches for third-party AI services insurers rely on.
Q: How do we handle 50 different state breach notification requirements?
A: The incident response section includes state notification mapping and procedures for managing multi-state compliance in breach scenarios.
What's Inside
Insurance AI Threat Landscape
- Model theft: Competitors stealing pricing algorithms, underwriting rules, claims triage models
- Data poisoning: Fraudulent claims designed to corrupt fraud detection AI, telematics manipulation
- Policyholder data exposure: PHI, financial data, claims details
- Distributed access risks: Agent/broker/TPA access patterns
Security Architecture for Insurance AI
- Policyholder data protection controls
- Agent/broker access management
- TPA and reinsurer data sharing security
- Legacy system integration security
Regulatory Compliance Controls
- GLBA Safeguards Rule requirements for AI
- NYDFS 23 NYCRR 500 compliance
- State breach notification requirements
- Examination readiness
Third-Party AI Vendor Security
- Security assessment for ISO, Verisk, LexisNexis
- Contract security requirements
- Data handling verification
- Ongoing monitoring
Claims Fraud AI Protection
- Fraud detection model security
- Training data integrity controls
- Adversarial attack prevention
- Model monitoring
Actuarial Model Security
- Pricing model protection
- Access controls for sensitive algorithms
- Model theft prevention
- Intellectual property protection
Incident Response for Insurance
- Insurance-specific incident classification
- Regulatory notification requirements by state
- Policyholder notification procedures
- Commissioner communication
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