Policy Framework for Deepfake Regulations in the state of Massachusetts
Thrilled to continue my role as a Cybersecurity Mentor at the Mass Cyber Center. This volunteer initiative is incredibly close to my heart as I strive to support the next generation of cybersecurity professionals and be the mentor I wish I had during the early stages of my career.
In this spring 2025 cohort, I'm mentoring a student from the Bridgewater State University, Massachusetts, on a very timely and impactful initiative — drafting a proposal for a new Privacy Policy focused on deepfake regulation in the state of Massachusetts.
While my past work as a cybersecurity mentor has largely involved designing, building, securing, and tearing down virtual machines using AWS, or managing Docker-hosted web servers, this year’s mentorship took a meaningful turn.
My mentee expressed a strong interest in cybersecurity policy development. Together, we’ve been analyzing the Commonwealth’s Enterprise Use and Development of Generative AI Policy, identifying critical gaps — especially around synthetic media and deepfakes — and shaping a legal and ethical framework to address AI-driven impersonation and misinformation, which has become one of the most urgent challenges of our time.
Here are the critical gaps that we have identified in the current Massachusetts Generative AI Policy that need to be addressed—especially in the context of deepfakes, misinformation, and public risk:
1. No Explicit Mention or Definition of Deepfakes
The policy discusses “generative AI” broadly but does not define or address deepfakes or synthetic media.
There's no classification of types of generated content (text vs. voice vs. video), which have different risk profiles.
This leaves ambiguity in applying the policy to high-risk media like fake videos of politicians or impersonated audio in scams.
2. No Labeling or Disclosure Requirements
There are no mandates for:Watermarking, Metadata tagging or Source transparency
This allows AI-generated content to be shared without identification, which increases risks of misinformation, manipulation, and fraud.
3. Lacks Regulation Around Consent and Biometric Use
The policy does not address consent requirements for using real people’s: Faces, Voices, Mannerisms
This is particularly critical for non-consensual deepfake videos, identity spoofing, and voice cloning in cybersecurity attacks.
4. No Detection or Verification Guidelines
There is no guidance or support for deploying deepfake detection systems in: Newsrooms, Government agencies, Election security, Enterprise fraud prevention
Without detection systems, agencies may unknowingly use or be misled by deepfakes.
5. No Enforcement or Penalties
The policy is largely advisory, lacking: Enforceable standards, Penalties for misuse, Accountability mechanisms
This limits its impact in preventing malicious or unethical applications of generative AI.
6. Limited Scope Beyond Government Use
The policy focuses on state agencies and contractors, not the broader public or private sector.
It does not regulate or provide guidance to: Private companies developing AI tools, Public users, Social media platforms or creators
7. No Emergency Response or Escalation Framework
There is no mechanism for rapid response when harmful AI-generated content (e.g., deepfakes) circulates.
No inter-agency collaboration protocols for managing crises caused by synthetic content.
The current Massachusetts Generative AI policy provides a strong foundation for ethical AI development in state government, but it lacks the depth and specificity needed to:
Confront the unique risks of deepfakes
Protect the public from misinformation and synthetic identity abuse
Establish clear guidelines, enforcement, and safeguards across sectors
In the context of Massachusetts, where innovation and technology are at the forefront, especially in the fields of AI, cybersecurity, and digital media, the state needs a clear and robust regulatory approach.
