The Intersection of Cybersecurity and AI Governance

Keith Freeman

A man is using a computer at his desk because he understands that a secure network and a solid AI plan are essential for the deployment of AI technologies.

October is Cybersecurity Awareness Month, sponsored by the Cybersecurity and Infrastructure Security Agency and the National Cybersecurity Alliance. In this article, a Crowe specialist offers insight on the critical importance of simultaneously establishing clear and effective AI governance and security practices.

AI is here. Deploying it securely requires a solid cybersecurity and AI governance plan.

As AI systems become increasingly embedded in digital infrastructure, the critical intersection of cybersecurity and AI governance is clear. Organizations that want to take full advantage of AI technologies should operate from a strategic, dual imperative: to fortify their digital ecosystems against emerging threats and embed a culture of security within their AI development and application processes.

Effective AI governance requires a robust security posture that protects against conventional cyberthreats and to develop and deploy AI technologies in ethical, transparent, and responsible ways. Failing to prioritize cybersecurity and governance at all levels of AI development and use could have severe repercussions, including data breaches, penalties, and for businesses, reputational damage. When it comes to embracing the future, a secure network is fundamental for the successful deployment of these powerful new technologies.

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Following are specific strategies that organizations can employ to mitigate security risk while exploiting the full potential of AI technologies.

Establish AI governance

Effective governance is the cornerstone of successful AI integration. Organizations need to establish a robust governance framework and define clear policies and guidelines regarding AI use. Frameworks should address data management, model development, ethical considerations, and regulatory compliance. Guidance for reference and alignment might include:

International Organization for Standardization (ISO): ISO/IEC 42001:2023
Artificial Intelligence Governance and Auditing: (AIGA): “AIGA AI Governance Framework”
National Institute of Standards and Technology (NIST): NIST-AI-100-1 “Artificial Intelligence Risk Management Framework”
NIST-AI-600-1 “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile”
Google: “A Policy Agenda for Responsible Progress in Artificial Intelligence”

Additionally, establishing a governance committee composed of cross-functional leaders and stakeholders (such as chief executive, technology, risk, and privacy officers, AI and machine learning leaders, ethics officers, and industry specialists) can help businesses align their AI initiatives with corporate values and legal standards.

Prioritize data integrity and security

AI systems can only be as good as the information they process, so data quality, integrity, and security are essential. Organizations must implement stringent data management practices that include secure data storage, regular audits, and compliance checks with information security regulations such as, but not limited to, the European Union’s General Data Protection Regulation and the Health Insurance Portability and Accountability Act. Encryption, access controls, and vulnerability assessments are essential components that can help protect data against unauthorized access and breaches.

Develop transparent AI models

Transparency in AI operations can increase trust and accountability, so organizations should strive to develop AI models that are explainable and transparent. Methods that allow stakeholders to understand and audit the decision-making processes of AI systems can help build confidence among users and facilitate easier identification and rectification of biases and errors in AI algorithms.

Continually monitor AI systems

To manage risks effectively, organizations must continually monitor AI systems. Monitoring can include deploying specific tools that track the performance, health, and outputs of AI applications in real time. This proactive surveillance helps quickly identify and mitigate any issues that could lead to operational failures or ethical concerns, such as biased decision-making or privacy infringements.

Foster an AI-literate workforce

It is crucial to educate and train employees on the use of AI technologies. An AI-literate workforce can better collaborate with AI systems and participate actively in the risk management process. Training programs should cover aspects of AI ethics, working with AI tools, and understanding AI outputs. Employees with AI knowledge can enhance operational efficiency and help promote a culture of security and compliance.

Prepare for AI-specific regulations

As AI technologies evolve, so will regulations associated with their development and use. Organizations must stay informed about constantly changing AI regulations and prepare to adapt their policies and procedures accordingly. This proactive regulatory compliance can help organizations avoid legal pitfalls and position themselves as responsible leaders in AI development and use.

Establish AI risk management teams

Dedicated risk management teams specializing in AI risk identification, assessment, and mitigation can enhance an organization’s ability to foresee and potentially forestall AI-related risks. These teams should work in tandem with AI developers, data specialists, and business units to identify potential risks at every stage of AI implementation, from data collection to model deployment and beyond.

Implement ethical AI standards

Adhering to ethical standards in AI deployment is vital. Organizations should commit to ethical AI development by integrating fairness, accountability, and transparency into their AI systems, including regular ethical audits, reviews of AI decisions for biases, and respect for user privacy and rights. Following ethical AI practices can help organizations maintain public trust and avoid reputational damage.

Standards such as ISO/IEC 42001:2023 and frameworks such as NIST-AI-100-1 and NIST-AI-600-1 include guidelines for many of these listed activities. Both ISO and NIST emphasize complying with privacy laws and safeguarding AI systems against threats by addressing key concerns such as data protection and the ethical, transparent use of AI technologies.

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Manage risk through cybersecurity and AI governance

Establishing robust AI governance, prioritizing data security, conducting AI-specific training, providing transparency, and adhering to regulatory and ethical standards are all indispensable practices for paving the way to successful and sustainable AI integration. By acknowledging the indispensable role of a secure network, organizations can develop and implement AI solutions that are innovative, resilient, ethical, and trusted.

The trajectory of AI is moving toward greater integration into the digital infrastructure. To meet this challenge, organizations should improve their overall security posture and embed effective security measures into their AI strategies. By embracing a well-thought-out, comprehensive methodology, organizations can safeguard against cyberthreats and deploy AI technologies responsibly, ethically, and effectively.

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