Introduction
Adversarial machine learning (AML) explores the vulnerabilities of machine learning models to various attacks and the strategies for defending against these threats [2]. It categorizes threats into those affecting Predictive AI and Generative AI [2], highlighting the legal and operational challenges posed by these vulnerabilities.
Description
Adversarial machine learning (AML) examines the vulnerabilities of machine learning models to various attacks and the strategies for defending against such threats [2]. It identifies two primary categories of threats: those affecting Predictive AI and those impacting Generative AI [2]. In Predictive AI [1] [2], privacy compromise attacks [1] [2], such as data reconstruction [1] [2], membership inference [1] [2], and model extraction [1] [2], can force models to disclose sensitive information [1] [2], leading to significant legal implications [1] [2], including data breaches [2]. For instance [1], a data reconstruction attack on a model that differentiates between valid and invalid drivers’ licenses could trigger legal obligations for notification under state data breach laws [2].
In the realm of Generative AI, prompt injection attacks are particularly concerning [2], as they manipulate model inputs to produce harmful outputs [1] [2], potentially exposing organizations to legal risks [1], including liability for misuse. The presence of AML threats complicates third-party diligence obligations [2], especially for entities governed by regulations like Gramm-Leach-Bliley and HIPAA [1]. These regulations require organizations to carefully assess service providers’ capabilities to protect customer information [2], yet the definition of “appropriate safeguards” in relation to AML risks remains ambiguous [2].
To navigate this uncertain legal landscape [2], organizations should adopt risk-reduction strategies [2], including targeted inquiries about AML risks during mergers and acquisitions [2], particularly when AI/ML models are integral to the transaction’s value [1]. Entities claiming complete mitigation of AML risks should be approached with caution [2], as research indicates that such claims are often overstated [2].
Contractual provisions can also be utilized to manage risk effectively [2]. In M&A transactions [1] [2], sellers may need to exclude AML risks from indemnification clauses [2], while buyers might seek indemnification for breaches related to AML [2]. Ensuring that AI/ML vendors address AML risks should be a key component of compliance programs [2], particularly when handling sensitive data [1] [2].
Organizations should revise their internal policies to reflect the reality that the risk of privacy compromise in AI/ML models may never be entirely eliminated [2]. Policies should inform individuals whose data is included in training sets about the potential for exposure due to AML attacks and establish guidelines for utilizing external servers in dataset creation [2].
These strategies serve as a foundational approach to managing adversarial machine learning risks [1], which are expected to evolve alongside advancements in mitigation techniques and the tactics employed by threat actors [1].
Conclusion
The exploration of adversarial machine learning underscores the critical need for robust risk management strategies to address the evolving threats to AI models. Organizations must remain vigilant in their compliance efforts, continuously updating policies and contractual agreements to mitigate potential legal and operational impacts. As AML threats and mitigation techniques advance, staying informed and proactive is essential to safeguarding sensitive information and maintaining regulatory compliance.
References
[1] https://www.jdsupra.com/legalnews/adversarial-machine-learning-in-focus-5292907/
[2] https://www.lexology.com/library/detail.aspx?g=1e295595-0022-4c00-90a6-88b2e3258a7c
