Introduction
As privacy and artificial intelligence (AI) regulations continue to evolve at a breakneck pace, cybersecurity leaders face mounting pressure to adapt. Privacy is no longer just a compliance checkbox—it’s a strategic imperative that must be embedded into every facet of an organization’s operations. With new privacy laws emerging across states and countries, including 20 U.S. state privacy laws, alongside groundbreaking AI regulations like California’s recent Transparency in Frontier Artificial Intelligence Act (TFAIA) and the EU AI Act, the stakes have never been higher. Cybersecurity leaders must not only safeguard data but also make certain that privacy principles like transparency, consent, and accountability are seamlessly integrated into their systems and processes. This summary examines the critical need for privacy by design, strategies to stay ahead of emerging regulations, and how to align privacy across business units, offering actionable insights to help organizations thrive in this complex environment.
Understanding the Need to Build Privacy by Design
Privacy is no longer a reactive, compliance-only effort but must be embedded proactively into organizational processes, much like cybersecurity. Historically, companies approached privacy as a “minimum viable product” to meet regulations like General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). However, with the rapid evolution of privacy laws, such as the eight new state laws in 2025 alone and the anticipated ninth in Massachusetts, privacy must now be a core, integrated function. Key principles, like data minimization, purpose limitation, and transparency, are essential. For example, organizations should only collect data necessary for specific purposes, reducing risks and storage burdens. Privacy audits, risk assessments, and data mapping are critical tools for compliance and accountability. These efforts not only help mitigate regulatory risks but also build consumer trust and strengthen organizational resilience.
Staying Ahead of Emerging Regulations in Privacy & AI
The regulatory landscape for privacy and AI is becoming increasingly complex, with more than 1,000 AI-related laws proposed in 2025 alone. California recently enacted the first AI-specific law in the U.S., while the EU AI Act and NIST AI Risk Management framework set global benchmarks. Emerging regulations emphasize transparency, consent, and accountability in AI systems, particularly around automated decision making and sensitive data processing. For instance, the Federal Trade Commission (FTC) has penalized companies for using unconsented data in AI models. Organizations must make sure that AI governance programs align with privacy principles, including clear documentation of data usage, robust consent mechanisms, and safeguards against profiling minors or making opaque decisions. In addition, universal opt-out mechanisms, mandated by states like California and Colorado, require businesses to honor consumer preferences for data sharing and targeted advertising. Staying compliant demands more than tool implementation and an annual check, but rather involves continuous monitoring, testing, and updating of privacy controls.
Incorporating & Aligning Privacy With Innovation & AI
Privacy and innovation are not mutually exclusive; they can and should coexist. Privacy-enhancing technologies (PETs) are increasingly integrated into organizational workflows. For example, privacy-by-design principles can streamline AI governance by embedding consent and transparency into data models from the outset. Cross-functional collaboration between privacy, cybersecurity, and legal teams is essential to align privacy with innovation. This includes conducting joint privacy and cybersecurity risk assessments to help avoid duplication and confirm appropriate coverage. Data mapping and inventories are foundational for both privacy and AI compliance, enabling organizations to track data flows, achieve accuracy, and respond to consumer requests effectively. In addition, third-party vendor management is critical, as organizations remain accountable for their vendors’ data practices. Making sure that contracts include standardized privacy clauses and compliance requirements can help mitigate risks.
Key Takeaways for Cybersecurity Leaders
- Proactive Privacy Integration: Embed privacy into organizational processes, treating it as a continuous, evergreen program rather than a one-time compliance effort.
- Regulatory Awareness: Stay informed about evolving privacy and AI laws, including state-specific regulations and global frameworks like the EU AI Act.
- Cross-Functional Collaboration: Foster partnerships between privacy, cybersecurity, and legal teams to help streamline risk assessments and compliance efforts.
- Consumer Trust: Help build trust by demonstrating transparency, honoring opt-out requests, and safeguarding sensitive data, including children’s and biometric data.
- AI Governance: Align AI innovation with privacy principles, helping ensure transparency, consent, and accountability in automated decision making.
Adopting these strategies can help cybersecurity leaders navigate the complex intersection of privacy, AI, and compliance while fostering innovation and trust.
How Forvis Mazars Can Help
Interested in learning more about these topics? Watch our archived webinar, “What Cybersecurity Leaders Need to Consider for Privacy & AI Compliance” or reach out to a professional at Forvis Mazars.