If you work with genetic or biometric data, you operate in one of the most sensitive (and fastest growing) risk landscapes today. Artificial intelligence (AI) is accelerating the use cases for how this data can be analyzed, shared, and monetized. Privacy regulations like CCPA are also expanding how that data needs to be governed, protected, and explained. But genetic and biometric data are unlike other types of personal data. It can’t be reset like a password. When compromised, the risk is forever. This reality is catching the eye of regulators while raising the stakes for how companies handle both cyber and privacy risk.
CCPA language has been expanded via CPRA to directly address “sensitive personal information”, automated decision-making, and risk assessments. AI use cases are now squarely in scope.
CCPA and AI: Identifying the Traits That Influence Data Privacy Risks
AI systems built on genetic and biometric data introduce a different class of risk. These systems do not just store or transmit data. They derive meaning from it.
This means detecting people, forecasting health events, and creating insights that could be considered new personal information under the CCPA’s expansive definition. Consider, too, that industry-specific laws bring their own nuances:
- HIPAA regulates protected health information (PHI) when it’s used in a healthcare setting.
- The Genetic Information Nondiscrimination Act (GINA) regulates how genetic information can be used with regard to health insurance and employment.
- Illinois’ Biometric Information Privacy Act (BIPA) places strict requirements on consent and use.
CCPA does not replace these frameworks. It overlaps with them. That overlap is where organizations often need clarity on how to comply.
Why AI Privacy Risk Is a Cross-Functional Responsibility Under CCPA
A single team rarely owns AI systems using genetic and biometric data.
Data may originate in clinical systems, be processed in analytics platforms, and ultimately inform business or consumer-facing decisions. Do these activities pose a considerable risk to consumers? Activities that include profiling or automated decision-making are considered high risk under CCPA. Risk and accountability are now distributed throughout your organization. Responsibility for managing risk now lives beyond IT and compliance teams. It lives with leaders and executives, legal, product, marketing, and security.
Accountability at the Top: Executive Leadership on AI, Risk and CCPA
Balancing privacy concerns with business innovation is challenging enough at the executive level; now add AI to the mix. Organizations will derive clear business value from AI projects that leverage biometric or genetic data. But they’ll also be accountable for regulatory responsibilities related to protecting consumer privacy, ensuring transparency in AI decision-making, and managing risk. Accountability starts at the top. Updates to CCPA require organizations to assess risk based on processing activities. Organizations may also need to demonstrate cybersecurity risk assessments for high-risk processing. Executive leaders can expect to be held accountable for how and why an organization collects and uses personal information.
5 CCPA Considerations for AI and Legal, Compliance Teams
The regulatory landscape is shifting, and it’s complicated.
With CCPA, consumers can request access to their personal information, request deletion of their personal information, and request information on how their personal information is being processed. These requests also apply to sensitive personal information like biometric or genetic data. Automated decision-making and profiling rules are still being developed. But we do know that organizations will be required to disclose these activities to consumers, and likely provide an opt-out.
The challenge with AI and CCPA is that many AI systems are not designed with the ability to provide insight into decision-making. Meeting CCPA requirements will take coordination between legal, technical, and commercial teams. Genetic data and biometric identifiers are valuable data sets. When centralized into AI databases and systems, they create cybersecurity risk beyond traditional security controls.
Once biometric or genetic data is exposed through a breach, consumers can’t change their DNA sequence or fingerprints like they can a password. We’ve already seen breaches involving biometric identifiers and genetic testing companies. Cybersecurity ties back to privacy. We’re seeing regulators start to link what constitutes reasonable cybersecurity expectations to privacy harms.
AI Development and Data Risk: CCPA Implications for Product and Data Teams
Product and data teams play a central role in shaping AI risk.
Decisions about how genetic and biometric data are collected, combined, and used can introduce exposure long before a product reaches the market.
This includes:
- Training models on sensitive datasets
- Generating inferences that may qualify as personal information
- Using data beyond its original intended purpose
Under CCPA, data inferred and behavioral insights may still fall within the definition of personal information.
AI Personalization isn’t prohibited by the CCPA, but here’s where businesses should proceed with caution
AI applications that personalize experiences or improve outcomes by using consumers’ biometric or behavioral data are going to draw more scrutiny. This is natural, as companies seek to leverage new technologies to improve operations, profits, and outcomes for consumers. But consumers still have a right to know what data is being used about them and how it’s being used to make decisions. If your organization deals with highly sensitive personal information like biometric or genomic data, transparency should be your goal.
Top AI Privacy Concerns with CCPA: Collection, Explainability, and Consumer Control
When we talk to organizations across the genetic and biometric data ecosystem, a few risks keep popping up:
- Lack of visibility into data lineage
- Inability to delete or isolate data used in AI models
- Limited explainability of AI-driven outcomes
- Overcollection of sensitive data
- Fragmented governance across teams
These risks are compounded by the permanence and sensitivity of the data involved.
When exposure occurs, the impact is not temporary. It is enduring.
How to Manage AI Privacy Risk and Meet CCPA Compliance Requirements
Organizations Leading on AI Privacy Risk Are Taking an Integrated Approach
Companies that take a proactive, integrated approach to AI privacy risk are mapping out data flows through AI systems to understand where risk lies and where to put controls throughout the AI lifecycle. They’re applying risk management and security testing to AI-specific elements like APIs, cloud environments, and data pipelines in addition to typical infrastructure. And they’re crossing organizational silos to align teams for consistent governance and decision-making. Learn how organizations can address AI and CCPA risk with integrated cybersecurity and privacy services.
Webinar A Practical Guide to Governing Native AI, Browser-Based AI, and Third-Party AI Tools
Building a Robust Privacy Program in the Age of AI
Why companies that collect genetic and biometric data need an integrated approach to risk. HALOCK supports this through services that align cybersecurity, privacy, and compliance:
- CCPA privacy services focused on data mapping, governance, and risk assessments
- AI risk assessments that evaluate how data is used, where exposure exists, and how to reduce it
- Penetration testing or offensive security that identifies vulnerabilities across AI ecosystems and connected environments
- Cyber risk advisory services grounded in frameworks like DoCRA (Duty of Care Risk Analysis) and CIS RAM
The Future of AI and CCPA: Building Trust Through Data Governance and Transparency
CCPA is reshaping how organizations are held accountable for accessing and managing some of the most private data.
For organizations working with genetic and biometric data, the data is more sensitive. Risks are here to stay, and expectations will only grow. Those that win will be the organizations that establish trust via transparency, governance, and data usage understanding.
In this environment, protecting data is not just about security. It is about responsibility and exercising your duty of care.
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