The Security Standard That Most Systems Miss
Risk adjustment platforms handle some of the most sensitive data in healthcare: clinical notes, diagnosis codes, member-level health information, and the AI-driven coding decisions that determine billions in federal payments. Every major data breach headline in healthcare involved the same categories of information sitting in these systems. Yet most health plans evaluate risk adjustment technology on coding capability first and security posture second, if they evaluate it at all.
The disconnect is structural. Procurement teams focus on what the platform does (chart review, coding, RADV response). Security teams focus on whether it meets baseline certifications (SOC 2, HIPAA compliance). Nobody asks the harder question: Does this platform meet the security standard required for a system that handles PHI at scale and produces evidence that federal auditors will examine?
CMS’s expanded audit program, which now covers all 550+ MA contracts annually, means that every piece of data in the risk adjustment platform is potentially subject to federal review. The platform isn’t just an operational tool. It’s an evidence repository that CMS may inspect. That changes the security calculus.
Three Vulnerabilities Plans Overlook
The first is access control granularity. Most platforms authenticate users at the system level: you’re either in, or you’re not. But risk adjustment workflows involve multiple roles with different data needs. Coders need access to clinical notes and AI recommendations.
Quality reviewers need access to coding decisions and evidence trails. Audit response teams need access to submission packages. Compliance officers need access to AI decision logs. When every role has access to everything, the attack surface for data exposure expands, and the compliance risk from unauthorized internal access increases.
The second is AI model security. The AI powering coding recommendations is trained on clinical data. If the training data is compromised, poisoned, or extracted, the plan’s proprietary clinical intelligence is exposed. Few plans ask vendors how their AI models are secured: where training data is stored, who has access to model weights, how model updates are validated before deployment, and whether the system is protected against adversarial inputs that could manipulate recommendations.
The third is evidence trail integrity. RADV evidence trails are legal documents that may be submitted to federal auditors. If those trails can be modified after creation, whether through system vulnerability or insider access, the plan can’t guarantee the integrity of its audit defense.
Evidence trails should be immutable once created: append-only, timestamped, and cryptographically verifiable. Most platforms don’t enforce this level of integrity protection.
What CMS’s Audit Expansion Means for Security
When CMS audits a plan’s submitted codes, the plan produces evidence from its risk adjustment platform. If that platform’s security posture is inadequate, the evidence itself is compromised. An auditor who discovers that evidence trails are modifiable, that access controls are weak, or that the AI’s decision logic lacks integrity protections may question whether the evidence presented is reliable.
The OIG’s February 2026 compliance guidance warned about ungoverned AI in risk adjustment. The guidance applies to clinical AI governance, but the principle extends to the infrastructure that houses and secures that AI. A system where the AI’s recommendations are explainable but the underlying infrastructure is insecure doesn’t meet the governance standard regulators are signaling.
Plans with HITRUST-certified, SOC 2 Type II compliant platforms operating in secure cloud environments (such as Azure’s healthcare-specific infrastructure) have a demonstrable security posture. Plans running risk adjustment on platforms without these certifications are carrying infrastructure risk alongside their coding risk.
The Security Evaluation That Should Happen
Plans evaluating or re-evaluating their risk adjustment platform should run a security assessment alongside the capability assessment. Test access control granularity, AI model security provisions, evidence trail immutability, data encryption standards, and disaster recovery architecture.
Ask for HITRUST certification status, SOC 2 Type II audit reports, and penetration testing results. The platform holds the plan’s most sensitive clinical data and produces the evidence federal auditors will review. Its security posture should match that responsibility.
