Best Practices: How to Communicate the Inter-Relationship of Generative AI and Cybersecurity Investments to Investors

The rapid scaling of generative artificial intelligence (AI) is transforming industries by automating tasks, enhancing decision-making processes, and driving innovation, while also driving a significant increase in data breaches, leading to higher costs and a surge in cybersecurity investments for public companies. It is critical for public companies to communicate this interplay between generative AI and data breaches as it relates to their business, technology and capital allocation frameworks in order to elevate their valuation with institutional investors.

The Scaling of Generative AI and Its Impacts

Generative AI is being widely adopted across sectors for its ability to generate human-like text, create sophisticated data models, and automate complex processes. According to a report by PwC, AI could contribute up to $15.7 trillion to the global economy by 2030. However, as AI technologies become more pervasive, they also become prime targets for cyberattacks.

Increase in Data Breaches

Data breaches have surged in frequency and severity over the past period. The IBM Cost of a Data Breach Report 2023 indicates that the global average cost of a data breach reached $4.45 million in 2023, a 2.3% increase from the previous this trend underscores the growing threat landscape that companies must navigate and invest in.

The Cost Dynamics of Generative AI and Data Breaches

1. Cost of Scaling Generative AI:

• Infrastructure and Development: Deploying generative AI at scale requires significant investment in infrastructure, including high-performance computing, data storage, and development tools. According to Gartner, AI infrastructure spending is expected to exceed $13 billion by 2025.
• Talent Acquisition: Hiring skilled AI professionals is costly, with AI specialists often commanding high salaries due to their expertise and the demand for their skills.

2. Cost of Data Breaches:

• Direct Costs: These include detection and escalation, notification, post-breach response, and regulatory fines. The IBM report highlights that these direct costs can account for over 50% of the total cost of a data breach.
• Indirect Costs: These include reputational damage, customer turnover, and lost business opportunities, which can have long-lasting impacts on a company’s financial health.

Generative AI Driving Increased Cybersecurity Investments

The dual pressures of scaling AI and mitigating data breaches are driving significant investments in cybersecurity. The global cybersecurity market is projected to grow from $217 billion in 2021 to $345 billion by 2026, according to key industry reports. Key areas of investment include:

• AI-Powered Cybersecurity Solutions: Leveraging AI to enhance threat detection, response capabilities, and predictive analytics to anticipate and mitigate cyber threats.
• Zero Trust Architectures: Implementing zero trust security models that assume no implicit trust and require continuous verification of identities and devices.
• Advanced Encryption Technologies: Utilizing advanced encryption methods to protect sensitive data both at rest and in transit.

Best Practices for Public Companies Communicating to Institutional Investors

To effectively communicate the strategic importance of these investments and their balanced and thoughtful capital allocation approach, public companies should consider the following best practices:

1. Transparent Communication:

• Clearly articulate the strategic rationale behind investments in AI AND cybersecurity and the balance. Explain how these investments align with the company's long-term growth objectives and risk management strategies.

2. Detailed Capex and Capital Allocation Framework:

• Provide a more specific breakdown of Capex investments, highlighting allocations towards AI infrastructure, cybersecurity enhancements, and talent acquisition. Demonstrate how these investments are expected to drive future revenue growth and operational efficiencies and expand margins.

3. Focus on Value Creation AND Risk Mitigation:

• Emphasize the potential for generative AI to unlock new revenue streams and enhance competitive positioning. Discuss the expected return on investment (ROI) from cybersecurity measures, not just in terms of risk mitigation but also in maintaining customer trust, safeguarding company assets and maintaining the brand.

4. Sustainability and Long-Term Vision:

• Align AI and cybersecurity investments with broader ESG goals, particularly in terms of data privacy and ethical AI use. Highlight the company’s commitment to sustainable growth and responsible innovation.

5. Regular Updates and Performance Metrics:

• Provide regular updates on the progress of AI and cybersecurity initiatives. Use performance metrics to demonstrate the impact of these investments on operational performance and risk reduction.

The interplay between the scaling of generative AI and the rise in data breaches is driving substantial and incremental cybersecurity investment. By effectively communicating the strategic importance of these investments and incorporating them into a comprehensive and forward looking capital allocation framework, public companies can enhance their valuation and build investor confidence. Adopting these best practices will not only help manage the risks associated with AI and data breaches but also position companies for sustained growth and innovation in an increasingly digital world.