Strategic Due Diligence for AI-Dependent Investors: Mitigating Third-Party Cybersecurity Risks in AI Infrastructure

The rapid adoption of artificial intelligence (AI) in infrastructure has created unprecedented opportunities for innovation, but it has also amplified exposure to third-party cybersecurity risks. For investors, the stakes are high: a single vulnerability in an AI-dependent supply chain can lead to data breaches, operational disruptions, or regulatory penalties. As AI systems become more integrated into critical operations-from financial modeling to healthcare diagnostics-the need for robust due diligence frameworks has never been more urgent.

The Escalating Threat Landscape

Recent studies underscore the growing sophistication of cyber threats targeting AI infrastructure. According to a report by Boston Consulting Group, 80% of chief information security officers (CISOs) now rank AI-powered cyberattacks as their top concern, a sharp increase from previous years. These attacks exploit generative AI to execute social engineering, fraud schemes, and data leakage, as research shows often bypassing traditional security measures. Compounding the issue, AI systems themselves are vulnerable to risks like model poisoning and prompt injection, which can corrupt training data or manipulate outputs.

Investors must recognize that third-party vendors-especially those managing AI infrastructure-can introduce systemic risks. A 2025 Harvard Law School study found that 72% of S&P 500 companies disclosed AI-related cybersecurity risks in their annual reports, with financial services and healthcare sectors leading the trend. This highlights a critical gap: while AI is a top investment priority, many organizations lack the expertise to secure it as research indicates.

Strategic Frameworks for Due Diligence

To address these challenges, investors must adopt a risk-based approach to third-party cybersecurity due diligence. Central to this is the integration of governance, risk management, and compliance (GRC) across vendor lifecycles. A 2025 FINRA report emphasized the importance of written supervisory procedures, including validating data protection controls in contracts and involving third-party service providers (TPSPs) in incident response testing. Similarly, the New York State Department of Financial Services (NYDFS) recommends classifying TPSPs by risk level and enforcing contractual terms that mandate AI-specific safeguards, such as data leakage prevention as guidance states.

Key elements of a mature due diligence framework include:
1. Centralized Risk Data: Aggregating third-party risk data into a unified platform to maintain visibility across the supply chain as case studies show.
2. Certifications and Compliance: Verifying vendor adherence to standards like SOC 2 or ISO 27001 as industry experts recommend.
3. Continuous Monitoring: Leveraging AI-powered platforms to detect emerging threats in real time as research demonstrates.
4. Fourth-Party Risk Assessment: Evaluating TPSPs' extended supply chains to identify indirect vulnerabilities as a 2025 report found.

AI as a Double-Edged Sword

While AI introduces risks, it also offers tools to mitigate them. A case study from Tribe demonstrated how an AI-powered research assistant reduced due diligence time from 7–10 days to under 1 day, while improving signal coverage by 30%. These systems automate data analysis, flag anomalies, and provide actionable insights, enabling investors to act proactively. For example, agentic AI agents can monitor vendor compliance statuses, public records, and threat intelligence feeds to identify risks before they escalate.

However, AI is not a panacea. The 2025 Pwn2Own AI category revealed critical vulnerabilities in foundational systems like Chroma DB and NVIDIA Triton Inference Server, underscoring the need for secure-by-design AI infrastructure. Investors must ensure vendors conduct regular vulnerability assessments and adopt frameworks like CISA's AI data security guidance, which emphasizes dataset verification and secure storage.

Post-Acquisition Execution: Turning Cyber Findings into Value

Due diligence is only the first step. Post-acquisition, investors must translate cyber findings into actionable deal terms. A 2025 AIMA report advised structuring purchase price adjustments, holdbacks, and representations and warranties based on identified risks. For instance, enforcing phishing-resistant MFA and deploying EDR tools within 100 days of acquisition can mitigate operational disruptions.

Cyber insurance must also align with these controls. Policies should avoid exclusions for AI-related incidents and include coverage for data recovery and regulatory fines as best practices indicate. Additionally, investors should mandate board-visible milestones, such as testing disaster recovery plans and hardening CI/CD pipelines, to ensure accountability as recommended by industry experts.

Conclusion: A Proactive Mindset for a High-Risk Era

Third-party cybersecurity risks in AI infrastructure are no longer peripheral concerns-they are valuation factors that directly impact deal outcomes. By adopting AI-driven due diligence frameworks, investors can move from reactive compliance to proactive risk management. The integration of AI in both threat detection and due diligence processes is not just a trend but a necessity in an era where cyberattacks are increasingly sophisticated and supply chains are more interconnected than ever.

For AI-dependent investors, the message is clear: cybersecurity must be treated as a strategic lever for value creation. Those who fail to prioritize it risk not only financial losses but also reputational damage in an industry where trust is paramount.