This year, the U.S. Securities and Exchange Commission (SEC) implemented rules on Cybersecurity Risk Management, Strategy, Governance, and Incident Disclosure for Public Companies. These rules require listed companies to disclose material cybersecurity incidents within four business days and provide ongoing disclosures related to cybersecurity risk management, strategy, and governance. As the December 15 compliance deadline approaches, public companies face the challenge of sharing more information about their cybersecurity policies, potentially benefiting both investors and threat actors seeking insight into attractive targets.

Public companies must navigate the dilemma of meeting regulatory demands while protecting sensitive information. Artificial intelligence (AI) emerges as a strategic solution, allowing companies to efficiently comply with rules and enhance overall cybersecurity. With the compliance deadline approaching, public companies are now poised to disclose unprecedented details about their cybersecurity policies, benefiting investors but also raising concerns about potential exploitation by threat actors. In this context, AI offers a strategic avenue, playing a crucial role in compliance and cybersecurity.

Although these benefits are significant, challenges also exist. AI technologies can play a crucial role in helping public companies meet rule requirements while enhancing security. Three key uses of AI include:

1. Incident Management and Disclosure: AI-powered tools can detect and investigate cybersecurity incidents in real-time, analyzing diverse data sources to identify suspicious activities. These tools, coupled with reinforcement learning, improve the company’s risk posture and facilitate timely and accurate incident reporting to the SEC.
2. Risk Management: AI-driven risk assessment tools analyze real-time data, identifying and prioritizing cybersecurity risks based on vulnerabilities, compliance areas, and third-party risks. These tools continuously monitor for vulnerabilities, compliance gaps, and policy issues, generating automated assessments and reports as required by the rules.
3. Governance: AI assists in establishing a robust cybersecurity governance framework by analyzing internal policies, external regulations, and industry best practices. It identifies gaps, ensures continuous improvement, and aligns the company with rule requirements, providing a comprehensive risk management framework.

However, despite all these benefits, AI technology also comes with its set of challenges.  AI-assisted outputs depend on the quality of training data, requiring risk leaders to use appropriate, unbiased datasets. Transparency in AI models is crucial to address potential biases, especially in nuanced regional and demographic data. Additionally, practitioners must stay vigilant for regulatory changes, such as those impacting AI technology, that may affect compliance.

In conclusion, while AI technology presents challenges, its proactive and strategic implementation is a necessary step for risk leaders to efficiently manage complex reporting requirements, ensuring the safety of company systems and assets in an evolving regulatory landscape.