COMMENTARY: As AI accelerates, a new class of tools is entering the fray: AI agents — systems that don’t just respond to prompts the way large language models (“LLMs”) such as OpenAI’s ChatGPT and Anthropic’s Claude do, but can independently interact with third-party systems (often through Application Programming Interface (“APIs”)) and execute multistep tasks to achieve user-defined goals. AI agents are designed to solve problems without the need for human oversight or prompting and are akin to personal assistants.While agentic AI opens up new paths for efficiency gains for private equity firms, it also introduces unique and heightened privacy and data protection concerns. The autonomy and independence of AI agents make them powerful; however, this autonomy and independence often requires elevated system access and unsupervised interactions with third-party data stores. Interested firms should recognize the regulatory and cyber implications up front, take steps to ensure they are piloting the right use cases, and embed agentic AI into operations in a controlled, auditable way.
Unlike LLMs that respond to discrete queries, AI agents often connect directly to various internal and external systems, such as email servers, file repositories, customer databases, and enterprise tools. These integrations and interactions increase the potential cyberattack surface, as vulnerabilities in any integrated system may be exploited to compromise the agent or its host environment.[SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Read more Perspectives here.]Because AI agents may autonomously take actions (e.g., send emails, modify databases, initiate transactions) in pursuit of a user’s directive, they pose a greater risk of accessing or disclosing personal information and other confidential or proprietary data in ways that the user did not anticipate or desire. This could run afoul of privacy laws such as the General Data Protection Regulation (GDPR) and U.S. state consumer privacy laws, which are premised on concepts such as transparency, consent, and data minimization and purpose limitation.These risks can be compounded by the fact that many AI agents’ decision-making processes are somewhat of a “black box” — meaning, they’re difficult for users to understand or explain — as agents may lack built-in tools for detailed logging, versioning, or audit trails. Even when AI models are built to show their “chain of thought” (i.e., how they came to a conclusion), models may not accurately reflect on their own reasoning.
How can agents be utilized to save time and money?
There’s significant opportunity emerging through agentic AI, such as increased efficiency and productivity, scalability of task automation, improved decision-making, and long-term cost savings. Private equity firms, in particular, are often resource constrained but workflow-heavy, with repetitive tasks that can benefit from automation.Here’s where AI agents can help:- Deal Sourcing & Screening: Agents can monitor databases, scan news feeds, and flag emerging acquisition targets that match specific investment criteria.
- Diligence Support: Agents can extract key terms from non-disclosure agreements, letters of intent, and financial statements and compare data across documents (e.g., EBITDA discrepancies, etc.)
- Investor Reporting & Communications: Agents can manage investor FAQ repositories, help draft investor reports and quarterly updates for Limited Partners, and track fund compliance deliverables.
- Internal Operations & Workflow Automation: Agents can manage human resources onboarding tasks, invoice processing, and manage scheduling. They can also auto-generate notes, emails, and financial data and can schedule follow-ups or assign action items.
