Velotix urges Congress to base privacy law on dynamic governance

Velotix says Congress should build a federal data privacy framework around real-time, machine-enforceable policy rather than static, state-by-state rules. The company argues that approach would reduce compliance friction while improving security as AI and changing enterprise data environments add new privacy risks.

Why it matters: - Velotix says a federal privacy law built around dynamic governance could help U.S. businesses reduce compliance costs, close security gaps and move data faster without weakening protections. - The company argues that static, manually enforced rules no longer match how modern enterprises manage employees, systems and sensitive data. - Velotix says a national standard could also help U.S. firms compete globally by making privacy controls more consistent and more usable.

What happened: - Velotix submitted a response to the U.S. House Committee on Energy and Commerce's Request for Information on a comprehensive federal data privacy and security framework. - CEO Dr. Adi Hod said Congress should base privacy policy on machine-interpretable, real-time enforceable controls instead of relying on state-by-state fragmentation. - The company framed its filing as a call for "actionable" policy that can be enforced across enterprise systems.

The details: - Velotix said any durable federal privacy framework should rest on three principles: machine-interpretable policy, real-time continuous enforcement and end-to-end traceability. - Machine-interpretable policy would let privacy rules be translated into technical controls across systems rather than reinterpreted manually for each platform. - Real-time enforcement would adjust access and protection as data, roles and regulations change, including during onboarding, role changes and employee departures. - End-to-end traceability would create a direct link between written policy and actual system permissions. - The submission also addressed AI's role in privacy, saying AI can help classify, monitor and protect data at scale. - Velotix also said AI systems need their own safeguards, including access limits, model transparency and auditing of how sensitive training data is used. - The company says its platform uses policy-based access control and AI-driven automation to reduce data access times from months to minutes. - Velotix said the platform continuously enforces policy across cloud, on-premises and hybrid environments and provides real-time visibility into data access and protection. - Velotix said it is a Selected Vendor in the Gartner Guide for Data Security Platforms and is ISO 27001 certified. - The company serves enterprises in financial services, healthcare, life sciences, telecommunications and other data-intensive industries. - Velotix's website is available at more information. - The company also shared a certification document at ISO 27001 certificate.

Between the lines: - Velotix is positioning privacy as an operational systems problem, not just a legal drafting problem. - The company's argument suggests that compliance frameworks will be more effective if they can adapt automatically as enterprise conditions change. - The emphasis on AI reflects a broader shift: businesses now need privacy rules that govern both data use by humans and data use by machine systems.

What's next: - Congress will decide whether to incorporate machine-enforceable, dynamic policy concepts into any federal privacy framework. - If lawmakers favor Velotix's approach, future rules could require closer alignment between written policy and technical enforcement. - Velotix says that shift would let organizations strengthen privacy while keeping access fast enough for day-to-day business use.

The bottom line: - Velotix wants federal privacy law to move from static compliance documents to systems that can enforce policy continuously in real time.