Addressing Insider Threats: CIA Analyst Divulges Confidential Information

One of the most difficult cybersecurity issues concerns predicting insider threats -- identifying the person or persons in your organization who are likely to divulge personal data or intellectual property.  The U.S. Department of Justice issued this press release recently: 

A former CIA analyst pleaded guilty today to retaining and transmitting Top Secret National Defense Information to people who were not entitled to receive it, information which was publicly posted on a social media platform in October 2024.

According to court documents, Asif William Rahman, 34, of Vienna, was an employee of the CIA since 2016 and had a Top-Secret security clearance with access to Sensitive Compartmented Information (SCI).

. . .

According to court documents, on Oct. 17, 2024, Rahman accessed and printed two Top Secret documents containing National Defense Information regarding a U.S. foreign ally and its planned actions against a foreign adversary. Rahman removed the documents, photographed them, and transmitted them to individuals he knew were not entitled to receive them. By Oct. 18, 2024, the documents appeared publicly on multiple social media platforms, complete with the classification markings.

After Oct. 17, 2024, Rahman deleted and edited journal entries and written work product on his personal electronic devices to conceal his personal opinions on U.S. policy and drafted entries to construct a false narrative regarding his activity. Rahman also destroyed multiple electronic devices, including a personal mobile device and an internet router he used to transmit classified information and photographs of classified documents, and discarded the destroyed devices in public trash receptacles in an effort to thwart potential investigations into him and his unlawful conduct.

Beginning in the spring of 2024 and continuing through November 2024, Rahman repeatedly accessed and printed classified National Defense Information, including documents classified up to the Top Secret level, to take them to his residence. There, Rahman reproduced the documents and, while doing so, altered them in an effort to conceal their source and his activity. Rahman then communicated Top Secret information that he learned in the course of his employment to multiple individuals he knew were not entitled to receive it.

Rahman was indicted by a grand jury on Nov. 7, 2024, and was arrested by the FBI as he arrived to work on Nov. 12, 2024. He has remained in custody since his arrest.

Rahman pleaded guilty to two counts of willful retention and transmission of classified information related to the national defense. He is scheduled to be sentenced on May 15, 2025. He faces a maximum penalty of 10 years in prison for both counts in the plea agreement. A federal district court judge will determine any sentence after considering the U.S. Sentencing Guidelines and other statutory factors.

U.S. Department of Justice Releases Final Rule on Protecting Personal Data in Bulk Transfers

The Department of Justice recently issued a final rule preventing access to U.S. citizens personal data.  The Press Release states, in relevant part:

. . . Today, the Justice Department issued a comprehensive final rule carrying out Executive Order (E.O.) 14117 “Preventing Access to Americans’ Bulk Sensitive Personal Data and United States Government-Related Data by Countries of Concern.” The E.O. charged the Justice Department with establishing and implementing a new regulatory program to address the urgent and extraordinary national security threat posed by the continuing efforts of countries of concern (and covered persons that they can leverage) to access and exploit Americans’ bulk sensitive personal data and certain U.S. Government-related data.. . .

“This final rule is a crucial step forward in addressing the extraordinary national security threat posed of our adversaries exploiting Americans' most sensitive personal data,” said Assistant Attorney General Matthew G. Olsen of the Justice Department’s National Security Division. “This powerful new national-security program is designed to ensure that Americans' personal data is no longer permitted to be sold to hostile foreign powers, whether through outright purchase or other means of commercial access.”

The Final Rule implements the E.O. by promulgating generally applicable rules for certain categories of data transactions that pose an unacceptable risk to the national security of the United States. As described in the E.O., countries of concern and covered persons can use their access to this data to engage in malicious cyber-enabled activities and malign foreign influence activities, bolster their military capabilities, and track and build profiles on U.S. persons (including members of the military and U.S. Intelligence Community, as well as other Federal employees and contractors) for illicit purposes such as blackmail, coercion, and espionage, and to bolster their military capabilities. Countries of concern and covered persons can also exploit this data to collect information on activists, academics, journalists, dissidents, political opponents, or members of nongovernmental organizations or marginalized communities to intimidate them; curb political opposition; limit freedoms of expression, peaceful assembly, or association; or enable other forms of suppression of civil liberties.

The Final Rule reflects the risk highlighted in the E.O. that the vulnerability of Americans’ bulk sensitive data is exacerbated because countries of concern are increasingly using bulk sensitive personal data to develop and enhance artificial intelligence (AI) capabilities and algorithms that, in turn, enable the use of large datasets in increasingly sophisticated and effective ways to the detriment of U.S. national security. Countries of concern can use AI in conjunction with multiple unrelated data sets, for example, to identify U.S. persons whose links to the federal government would be otherwise obscured in a single dataset and who can then be targeted for espionage or blackmail.

Among other things, the Final Rule identifies countries of concern and covered persons to whom the Final Rule applies, and designates classes of prohibited, restricted, and exempt transactions. The Final Rule establishes bulk thresholds for certain sensitive personal data, including human ‘omic data, biometric identifiers, precise geolocation data, personal health data, personal financial data, and certain covered personal identifiers. The Final Rule also prescribes processes to obtain licenses authorizing otherwise prohibited or restricted transactions; protocols for the designation of covered persons; and provides advisory opinions, and recordkeeping, reporting, and other due diligence obligations for covered transactions.

The Final Rule is consistent with the United States’ commitment to promoting an open, global, interoperable, reliable, and secure internet; protecting human rights online and offline; supporting a vibrant, global economy by promoting cross-border data flows that are required to enable international commerce and trade; and facilitating open investment. Notably, the Final Rule does not impose generalized data localization requirements regarding the physical or electronic storage of Americans’ bulk sensitive personal data or U.S. Government-related data, nor does it require locating computing facilities within the United States to process such data. The Final Rule does not prohibit U.S. persons from conducting medical, scientific, or other research in countries of concern, or from partnering or collaborating with covered persons to share data to conduct researching, if that activity does not involve the exchange of payment or other consideration as part of a covered data transaction. The Final Rule also does not broadly prohibit U.S. persons from engaging in commercial transactions, including exchanging financial and other data as part of the sale of commercial goods and services with countries of concern or covered persons, or impose measures aimed at a broader decoupling of the substantial consumer, economic, scientific, and trade relationships that the United States has with other countries.

The Final Rule further exempts several classes of data transactions from the scope of its prohibitions and restrictions, including personal communications and certain financial services transactions, corporate group transactions, transactions authorized by Federal law and international agreements, investment agreements subject to a Committee on Foreign Investment in the United States (CFIUS) action, telecommunication services, biological product and medical device authorizations, clinical investigations, and others.

The Final Rule’s prohibitions and restrictions are consistent with other access restrictions on sensitive personal data that have been imposed in other contexts, including transactions reviewed by the CFIUS and the Committee for the Assessment of Foreign Participation in the U.S. Telecommunications Services Sector (Team Telecom).

Lastly, under the Final Rule, parties engaging in vendor agreements, employment agreements, and investment agreements involving access by countries of concern or covered persons to bulk U.S. sensitive personal data or U.S. Government-related data would be restricted transactions that must comply with the separate security requirements that have been developed by the Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency (CISA) in coordination with the Justice Department. These security requirements include organizational and system-level requirements (such as ensuring that basic organizational cybersecurity policies, practices, and controls are in place), and data-level requirements (such as data minimization and masking, encryption, and privacy-enhancing techniques). These critical requirements will be published separately by CISA through the Federal Register and on CISA’s website.

In connection with the Final Rule, the Justice Department will publish compliance, enforcement, and other guidance, which will be located at www.justice.gov/nsd/data-security.. . .

Five Eyes on China: 60 Minutes

Members of the Five Eyes recently provided a brief overview of the threat of technology theft concerning China on the U.S. 60 Minutes show.  The interview includes brief mention of academic security as well as election influence.  The interview can be found, here. 

U.S. Copyright Notice of Inquiry Regarding AI and Copyright

The U.S. Copyright Office released today a notice of inquiry concerning AI and copyright.  The Press Release states:

Today, the U.S. Copyright Office issued a notice of inquiry (NOI) in the Federal Register on copyright and artificial intelligence (AI). The Office is undertaking a study of the copyright law and policy issues raised by generative AI and is assessing whether legislative or regulatory steps are warranted. The Office will use the record it assembles to advise Congress; inform its regulatory work; and offer information and resources to the public, courts, and other government entities considering these issues.

The NOI seeks factual information and views on a number of copyright issues raised by recent advances in generative AI. These issues include the use of copyrighted works to train AI models, the appropriate levels of transparency and disclosure with respect to the use of copyrighted works, the legal status of AI-generated outputs, and the appropriate treatment of AI-generated outputs that mimic personal attributes of human artists.

The NOI is an integral next step for the Office’s AI initiative, which was launched in early 2023. So far this year, the Office has held four public listening sessions and two webinars. This NOI builds on the feedback and questions the Office has received so far and seeks public input from the broadest audience to date in the initiative.

“We launched this initiative at the beginning of the year to focus on the increasingly complex issues raised by generative AI. This NOI and the public comments we will receive represent a critical next step,” said Shira Perlmutter, Register of Copyrights and Director of the U.S. Copyright Office. “We look forward to continuing to examine these issues of vital importance to the evolution of technology and the future of human creativity.”

Initial written comments are due by 11:59 p.m. eastern time on Wednesday, October 18, 2023. Reply comments are due by 11:59 p.m. eastern time on Wednesday, November 15, 2023. Instructions for submitting comments are available on the Office’s website. Commenters may choose which and how many questions to respond to in the NOI.

The NOI includes the following questions:

General Questions

The Office has several general questions about generative AI in addition to the specific topics listed below. Commenters are encouraged to raise any positions or views that are not elicited by the more detailed questions further below.

1. As described above, generative AI systems have the ability to produce material that would be copyrightable if it were created by a human author. What are your views on the potential benefits and risks of this technology? How is the use of this technology currently affecting or likely to affect creators, copyright owners, technology developers, researchers, and the public?

2. Does the increasing use or distribution of AI-generated material raise any unique issues for your sector or industry as compared to other copyright stakeholders?

3. Please identify any papers or studies that you believe are relevant to this Notice. These may address, for example, the economic effects of generative AI on the creative industries or how different licensing regimes do or could operate to remunerate copyright owners and/or creators for the use of their works in training AI models. The Office requests that commenters provide a hyperlink to the identified papers.

4. Are there any statutory or regulatory approaches that have been adopted or are under consideration in other countries that relate to copyright and AI that should be considered or avoided in the United States?  How important a factor is international consistency in this area across borders?

5. Is new legislation warranted to address copyright or related issues with generative AI? If so, what should it entail? Specific proposals and legislative text are not necessary, but the Office welcomes any proposals or text for review.

Training

If your comment applies only to a specific subset of AI technologies, please make that clear.

6. What kinds of copyright-protected training materials are used to train AI models, and how are those materials collected and curated?

6.1. How or where do developers of AI models acquire the materials or datasets that their models are trained on? To what extent is training material first collected by third-party entities (such as academic researchers or private companies)?

6.2. To what extent are copyrighted works licensed from copyright owners for use as training materials? To your knowledge, what licensing models are currently being offered and used?

6.3. To what extent is non-copyrighted material (such as public domain works) used for AI training? Alternatively, to what extent is training material created or commissioned by developers of AI models?

6.4. Are some or all training materials retained by developers of AI models after training is complete, and for what purpose(s)? Please describe any relevant storage and retention practices.

7. To the extent that it informs your views, please briefly describe your personal knowledge of the process by which AI models are trained. The Office is particularly interested in:

7.1. How are training materials used and/or reproduced when training an AI model? Please include your understanding of the nature and duration of any reproduction of works that occur during the training process, as well as your views on the extent to which these activities implicate the exclusive rights of copyright owners.

7.2. How are inferences gained from the training process stored or represented within an AI model?

7.3. Is it possible for an AI model to “unlearn” inferences it gained from training on a particular piece of training material? If so, is it economically feasible? In addition to retraining a model, are there other ways to “unlearn” inferences from training?

7.4. Absent access to the underlying dataset, is it possible to identify whether an AI model was trained on a particular piece of training material?

8. Under what circumstances would the unauthorized use of copyrighted works to train AI models constitute fair use? Please discuss any case law you believe relevant to this question.

8.1. In light of the Supreme Court's recent decisions in Google v. Oracle America   and Andy Warhol Foundation v. Goldsmith, how should the “purpose and character” of the use of copyrighted works to train an AI model be evaluated? What is the relevant use to be analyzed? Do different stages of training, such as pre-training and fine-tuning, raise different considerations under the first fair use factor?

8.2. How should the analysis apply to entities that collect and distribute copyrighted material for training but may not themselves engage in the training?

8.3. The use of copyrighted materials in a training dataset or to train generative AI models may be done for noncommercial or research purposes. How should the fair use analysis apply if AI models or datasets are later adapted for use of a commercial nature?  Does it make a difference if funding for these noncommercial or research uses is provided by for-profit developers of AI systems?

8.4. What quantity of training materials do developers of generative AI models use for training? Does the volume of material used to train an AI model affect the fair use analysis? If so, how?

8.5. Under the fourth factor of the fair use analysis, how should the effect on the potential market for or value of a copyrighted work used to train an AI model be measured?  Should the inquiry be whether the outputs of the AI system incorporating the model compete with a particular copyrighted work, the body of works of the same author, or the market for that general class of works?

9. Should copyright owners have to affirmatively consent (opt in) to the use of their works for training materials, or should they be provided with the means to object (opt out)?

9.1. Should consent of the copyright owner be required for all uses of copyrighted works to train AI models or only commercial uses? 

9.2. If an “opt out” approach were adopted, how would that process work for a copyright owner who objected to the use of their works for training? Are there technical tools that might facilitate this process, such as a technical flag or metadata indicating that an automated service should not collect and store a work for AI training uses? 

9.3. What legal, technical, or practical obstacles are there to establishing or using such a process? Given the volume of works used in training, is it feasible to get consent in advance from copyright owners?

9.4. If an objection is not honored, what remedies should be available? Are existing remedies for infringement appropriate or should there be a separate cause of action?

9.5. In cases where the human creator does not own the copyright—for example, because they have assigned it or because the work was made for hire—should they have a right to object to an AI model being trained on their work? If so, how would such a system work?

10. If copyright owners' consent is required to train generative AI models, how can or should licenses be obtained?

10.1. Is direct voluntary licensing feasible in some or all creative sectors?

10.2. Is a voluntary collective licensing scheme a feasible or desirable approach?  Are there existing collective management organizations that are well-suited to provide those licenses, and are there legal or other impediments that would prevent those organizations from performing this role? Should Congress consider statutory or other changes, such as an antitrust exception, to facilitate negotiation of collective licenses?

10.3. Should Congress consider establishing a compulsory licensing regime?  If so, what should such a regime look like? What activities should the license cover, what works would be subject to the license, and would copyright owners have the ability to opt out? How should royalty rates and terms be set, allocated, reported and distributed?

10.4. Is an extended collective licensing scheme  a feasible or desirable approach?

10.5. Should licensing regimes vary based on the type of work at issue?

11. What legal, technical or practical issues might there be with respect to obtaining appropriate licenses for training? Who, if anyone, should be responsible for securing them (for example when the curator of a training dataset, the developer who trains an AI model, and the company employing that model in an AI system are different entities and may have different commercial or noncommercial roles)?

12. Is it possible or feasible to identify the degree to which a particular work contributes to a particular output from a generative AI system? Please explain.

13. What would be the economic impacts of a licensing requirement on the development and adoption of generative AI systems?

14. Please describe any other factors you believe are relevant with respect to potential copyright liability for training AI models.

Transparency & Recordkeeping

15. In order to allow copyright owners to determine whether their works have been used, should developers of AI models be required to collect, retain, and disclose records regarding the materials used to train their models? Should creators of training datasets have a similar obligation?

15.1. What level of specificity should be required?

15.2. To whom should disclosures be made?

15.3. What obligations, if any, should be placed on developers of AI systems that incorporate models from third parties?

15.4. What would be the cost or other impact of such a recordkeeping system for developers of AI models or systems, creators, consumers, or other relevant parties?

16. What obligations, if any, should there be to notify copyright owners that their works have been used to train an AI model?

17. Outside of copyright law, are there existing U.S. laws that could require developers of AI models or systems to retain or disclose records about the materials they used for training?

Generative AI Outputs

If your comment applies only to a particular subset of generative AI technologies, please make that clear.

Copyrightability

18. Under copyright law, are there circumstances when a human using a generative AI system should be considered the “author” of material produced by the system? If so, what factors are relevant to that determination? For example, is selecting what material an AI model is trained on and/or providing an iterative series of text commands or prompts sufficient to claim authorship of the resulting output?

19. Are any revisions to the Copyright Act necessary to clarify the human authorship requirement or to provide additional standards to determine when content including AI-generated material is subject to copyright protection?

20. Is legal protection for AI-generated material desirable as a policy matter? Is legal protection for AI-generated material necessary to encourage development of generative AI technologies and systems? Does existing copyright protection for computer code that operates a generative AI system provide sufficient incentives?

20.1. If you believe protection is desirable, should it be a form of copyright or a separate sui generis right? If the latter, in what respects should protection for AI-generated material differ from copyright?

21. Does the Copyright Clause in the U.S. Constitution permit copyright protection for AI-generated material? Would such protection “promote the progress of science and useful arts”?  If so, how?

Infringement

22. Can AI-generated outputs implicate the exclusive rights of preexisting copyrighted works, such as the right of reproduction or the derivative work right? If so, in what circumstances?

23. Is the substantial similarity test adequate to address claims of infringement based on outputs from a generative AI system, or is some other standard appropriate or necessary?

24. How can copyright owners prove the element of copying (such as by demonstrating access to a copyrighted work) if the developer of the AI model does not maintain or make available records of what training material it used? Are existing civil discovery rules sufficient to address this situation?

25. If AI-generated material is found to infringe a copyrighted work, who should be directly or secondarily liable—the developer of a generative AI model, the developer of the system incorporating that model, end users of the system, or other parties?

25.1. Do “open-source” AI models raise unique considerations with respect to infringement based on their outputs? 

26. If a generative AI system is trained on copyrighted works containing copyright management information, how does 17 U.S.C. 1202(b) apply to the treatment of that information in outputs of the system?

27. Please describe any other issues that you believe policymakers should consider with respect to potential copyright liability based on AI-generated output.

Labeling or Identification

28. Should the law require AI-generated material to be labeled or otherwise publicly identified as being generated by AI? If so, in what context should the requirement apply and how should it work?

28.1. Who should be responsible for identifying a work as AI-generated?

28.2. Are there technical or practical barriers to labeling or identification requirements?

28.3. If a notification or labeling requirement is adopted, what should be the consequences of the failure to label a particular work or the removal of a label?

29. What tools exist or are in development to identify AI-generated material, including by standard-setting bodies? How accurate are these tools? What are their limitations?

Additional Questions About Issues Related to Copyright

30. What legal rights, if any, currently apply to AI-generated material that features the name or likeness, including vocal likeness, of a particular person?

31. Should Congress establish a new federal right, similar to state law rights of publicity, that would apply to AI-generated material? If so, should it preempt state laws or set a ceiling or floor for state law protections? What should be the contours of such a right?

32. Are there or should there be protections against an AI system generating outputs that imitate the artistic style of a human creator (such as an AI system producing visual works “in the style of” a specific artist)? Who should be eligible for such protection? What form should it take?

33. With respect to sound recordings, how does section 114(b) of the Copyright Act relate to state law, such as state right of publicity laws?  Does this issue require legislative attention in the context of generative AI?

34. Please identify any issues not mentioned above that the Copyright Office should consider in conducting this study.

It will be very interesting to see the responses.  I wonder if AI was used to help generate the questions.  I am sure someone will submit AI generated responses to the questions.  I do wonder about moral rights [fn. 38 in the document].