How AI is Impacting Private Equity Risk

In the article, Private Equity Cybersecurity: Portfolio Risk Management Guide [2026], we provide a clear outline on how to build an efficient and effective PE cybersecurity program. As we follow the evolution of AI within portfolio companies, it is worth highlighting the common cyber challenges companies are observing and addressing, and the overall impact to PE cyber risk.

1. User understanding of AI risks and responsibilities. The novelty of AI seems to have had a numbing effect on the cyber sensibilities of the average employee. Security Awareness tools have been surprisingly slow to provide AI centric content similar to phishing training that has been proven effective in reducing employee driven cyber events. Companies report: - Employees clicking on AI provided links. - Employees providing AI with credentials to company resources. - Employees performing work tasks using non-enterprise AI accounts.
   

2. Immature enterprise AI deployment tools. Tools designed to provide guardrails including data loss protections, prompt filtering, response filtering, etc. are relatively new and limited in functionality and performance. Companies report: - Tools do not perform consistently across data platforms (M365, Google Drive, etc.) - Users adapt to prompt filtering to bypass controls. - Slow response to evolving AI capabilities.
   

3. Limiting rogue AI access. AI access points (browser, extensions, SaaS embedded, etc.) have created a challenge for IT to direct AI queries to approved corporate AI models. Companies report: - The new "shadow IT" is unapproved or un-filtered AI access. - IT groups have had to consider tighter controls such as virtual desktops to fully control end user interactions with AI. -Logging capabilities to track user activity and data flows limited.
   

4. Demand for AI outpacing appropriate IT controls. The speed and broadness of AI demand has parallels to the initial corporate rollout of internet access or the evolution of SaaS where departments or users adopted software outside of IT's purview, but neither of these examples had the urgency of AI access. Companies report: - IT departments discovering user access to multiple AI models and deployment of agentic AI on corporate machines. -Department managers justifying subversion of IT controls due to "critical" need for AI tool access.

Private Equity: AI Checklist

1. Knowledge Sharing - Host AI Roundtable The immaturity of AI deployment tools and vendor lead promotion creates a challenging landscape for solution evaluation. It is likely that AI adoption leaders within the portfolio have valuable insight and experience to share.
   

2. Temper Demand - Communicate AI Strategy Provide portfolio company leadership with reasonable expectations of AI progress and highlight risks associate with failed or uncontrolled deployments.
   

3. Due Diligence - AI Awareness and Risk Include AI in due diligence and quickly assess post-aquisition. A month's old assessment may have quickly evolved and present un-assessed AI risks.
   

4. Cybersecurity Program - Include AI in Ongoing Assessments and Update Criteria as Appropriate Use cybersecurity program to communicate evolving risks and best-practices. Modify or enhance criteria based on AI impacts.
   

Balance Risk Concerns with Innovation

Overly restrictive AI controls may hamper AI adoption and innovation. Each company will have a unique AI risk profile depending on their data and industry, for example:

PII, Health Records, Tax and Financial Records, Payroll, Regulated Industries	High Risk
Retail, Consumer Services, Manufacturing, Distribution, Logistics	Low Risk

Private equity should adopt a flexible risk stance which provides control guidance when appropriate but allows for a less restrictive approach that promotes innovation where risk is lower.