AI adoption is accelerating in the financial services industry, both as an asset for improving business operations and as a potential tool to defend against cybercriminals. At the same time, adopting AI systems expands the attack surface that financial institutions must protect. Within this context, the NYDFS guidelines highlight the need for proactive risk management strategies that encompass the unique challenges posed by AI technologies.
Cybersecurity Risks of AI
The NYDFS guidance outlines several key cybersecurity risks associated with AI, along with strategies for mitigating those risks:
- AI-Enabled Social Engineering: One of the most immediate concerns is AI’s potential to enhance social engineering attacks. With tools like deepfakes—AI-generated media that can mimic real people—attackers can create highly convincing phishing schemes. These attacks may occur via emails, phone calls (vishing), SMS (smishing), or even video conferencing, where the attacker impersonates trusted employees or executives.
- AI-Enhanced Cybersecurity Attacks: AI allows cybercriminals to amplify the potency, scale, and speed of their attacks. With AI, attackers can quickly scan and analyze vast amounts of data, identify and exploit vulnerabilities, deploy malware, steal sensitive information more efficiently, and develop new malware variants or ransomware designed to evade detection.
- Exposure or Theft of NPI: Financial institutions increasingly rely on AI to process sensitive data, including personally identifiable information (PII) and financial records. This growing reliance heightens the risk of exposure or theft of non-public information (NPI), which is protected under the NYDFS Cybersecurity Regulation.
- Supply Chain Vulnerabilities: As financial organizations integrate AI into their operations, they also depend on a range of third-party vendors and partners. This interconnectedness introduces the risk of cyberattacks targeting vulnerabilities within the supply chain, including AI systems or software that may have been tampered with or compromised.
Mitigating AI Cybersecurity Risks: Key Strategies for Financial Institutions
The NYDFS’s guidance offers practical advice on how institutions can address these AI-specific threats and integrate them into their existing cybersecurity programs. Here are key strategies from the guidance:
- Risk Assessments and AI-Specific Programs: Under the NYDFS Cybersecurity Regulation, financial entities are required to perform regular risk assessments. According to NYDFS, these assessments must include AI-related risks. This involves not only evaluating the internal use of AI systems but also assessing the AI systems provided by third-party vendors. Institutions should also ensure that their incident response plans, business continuity plans, and disaster recovery strategies are tailored to handle AI-driven risks.
- Third-Party Service Provider Management: Given the interconnected nature of modern financial systems, managing third-party relationships is more critical than ever. Financial institutions must ensure that their third-party vendors—whether they are providing AI-powered services or supporting infrastructure—adhere to the same stringent cybersecurity standards. Regular assessments and audits should be conducted to ensure third-party systems remain secure.
- Access Controls: The NYDFS guidelines emphasize the importance of robust access control mechanisms, ensuring that only authorized personnel can access sensitive AI-driven systems. This includes implementing multi-factor authentication (MFA), role-based access controls (RBAC), and segmentation of sensitive data to reduce the impact of a potential breach.
- Cybersecurity Training: AI’s potential use in social engineering attacks makes cybersecurity awareness training more critical than ever. Institutions should regularly educate their employees about the risks of AI-enhanced attacks and equip them with the knowledge to identify and respond to potential threats. Employees must be trained to recognize the signs of AI-powered phishing attempts and social engineering tactics.
- Continuous Monitoring and Data Management: Financial institutions should implement real-time monitoring tools to detect anomalies and suspicious activities within their AI systems. AI-driven cybersecurity monitoring tools can help track and flag unusual patterns that could signal an ongoing attack or breach. Additionally, effective data management practices should ensure that sensitive data is encrypted, segmented, and protected against unauthorized access.
The Road Ahead: What’s Next for AI and Cybersecurity?
The NYDFS’s AI cybersecurity guidance underscores the need for financial institutions to proactively incorporate AI considerations into their risk management activities. While the guidelines focus on regulated entities, the risks and strategies outlined are universally relevant to many organizations using AI. As AI technologies become more pervasive, institutions of all sizes must also integrate AI-specific risks into their broader cybersecurity and risk management frameworks.
At HackerOne, we recognize that institutions need more than just traditional cybersecurity measures to address the growing risks posed by AI. That’s why we advocate for proactive, real-world testing through AI red-teaming.
Red-teaming is a form of adversarial testing that can reveal flaws such as the potential for hackers to bypass AI security protections, as well as algorithmic safeguards against unsafe or harmful output. HackerOne’s red-teaming is driven by a community of ethical hackers whose creativity and expertise help organizations around the world stay safer and more secure. By uncovering AI vulnerabilities and algorithmic flaws early, institutions can take steps to mitigate them before they can be exploited by bad actors.
As regulatory requirements around AI and cybersecurity come into focus, institutions should view the NYDFS guidelines not just as best practices but as business compliance imperatives. Securing AI systems is no longer optional; it’s essential for protecting both organizational assets and customer trust.