AI Data Security for Dealmakers: Avoiding Breaches, Leaks, and Risks

If you’re still thinking that artificial intelligence is in the distance, think again. The transformation isn’t a coming event; it’s already running your daily operations. Large language models (LLMs) have quietly embedded themselves into nearly every business tool you touch, from Excel’s new AI formulas to Slack’s smart summaries to the predictive text in your customer relationship management (CRM) tool. These LLM-powered features are delivering real productivity gains that have caught the attention of dealmakers worldwide.

But beneath those impressive efficiency numbers lurk security flaws, stealth web crawling operations, and cybersecurity vulnerabilities that could become a compliance nightmare.

Before 2022, hardly anyone had used, nor heard of, the term LLMs. Nowadays, the savviest investment firm would be paralyzed without them. From analyzing deal memos, summarizing market research, and even drafting preliminary valuations, companies are integrating LLM tools with existing enterprise systems like CRMs, enterprise resource planning (ERP) tools, and analytics platforms, creating seamless workflows that seem almost magical in their efficiency.

The temptation to adopt is well justified. Time-pressed analysts can use LLMs to generate comprehensive company profiles in minutes rather than hours. Due diligence teams can process massive document sets with unprecedented speed. So, yes, the productivity gains are real, and they’re compelling enough that firms are racing to implement these tools across the board.

But from both a dealmaker and a user’s perspective, the interesting thing is that not all AI implementations are created equal. The difference between a hastily integrated ChatGPT plugin and a secure, purpose-built deep research solution like Scholar is becoming a competitive advantage that sharp investors are learning to spot.

Perplexing Problems

The recent legal news surrounding companies like Perplexity highlight a growing concern that should keep every deal professional awake at night. Stealth web crawling and unauthorized data scraping aren’t just theoretical risks. When Perplexity faced lawsuits for its aggressive data collection practices, it exposed the uncomfortable reality that many AI tools operate in legal gray areas.

The issue touches on the fundamental question of knowing where your information actually comes from. When your AI assistant pulls information to support an investment thesis, can you verify where that data came from? More importantly, can you be sure you’re not inadvertently using proprietary information that could expose your firm to legal liability?

Experts at Cybersecurity Ventures predict that by 2025, cybercrime will cost the world $10.5 trillion annually, with much of the rise attributed to advanced technologies like LLMs. For dealmakers, this isn’t just a cost of doing business but a fundamental shift in how we evaluate risk.

Companies are making a calculated trade-off, accepting potential copyright and data sourcing risks in exchange for immediate productivity gains. But companies are starting to demand more transparency from their AI vendors. They want to know not just what the AI can do, but how it does it and whether that process can withstand legal scrutiny.

Security Blind Spots

Most firms are treating AI integration like any other software rollout, when in reality, it requires a completely different set of data security standards. Traditional cybersecurity focuses on keeping bad actors out. But with LLMs, you’re essentially inviting an AI agent to read your most sensitive data and potentially share insights derived from it.

The security landscape for LLMs is complex due to critical vulnerabilities such as data poisoning (where hackers sneak bad or misleading information into the data an AI learns from) and prompt injection attacks (where hackers trick an AI with sneaky instructions hidden in text). For investment firms handling confidential deal information, these aren’t abstract threats. A single compromised AI system could leak details about pending acquisitions, expose proprietary investment strategies, or accidentally cross-contaminate data between competing deals.

The top firms use tenant-specific databases and encryption key access controls, creating custom spaces for each deal so confidential information from one transaction doesn’t affect another. Yet many organizations still treat AI like glorified calculators, overlooking that they’re giving these systems access to their entire knowledge base.

The security standards that dealmakers should demand from AI vendors include robust data segregation, transparent data handling policies, and clear commitments about not using customer data for model training. Free AI tools that improve their algorithms by learning from user inputs should be treated with extreme caution in any deal environment.

Real Value Or Marketing Hype

For dealmakers looking for AI tools that deliver value, the challenge is telling real AI benefits apart from hype. Every software vendor is now claiming to offer AI capabilities, but not all implementations deliver trustworthy and meaningful returns.

Real enterprise value comes from AI tools that integrate deeply with existing workflows and provide verifiable, actionable insights. Take Scholar, our deep research product, as an example of how purpose-built AI should work. It creates comprehensive research reports by combining proprietary data with validated external sources, using agentic workflows to fact-check and synthesize information specifically for dealmaking contexts.

What sets Scholar apart isn’t just the AI model, but the whole system. It uses tenant-specific databases, keeps data strictly separated, and is fully transparent about sources. While generic AI treats every query the same, Scholar is purpose-built for the confidential, high-stakes world of M&A.

This represents a fundamentally different approach from companies that simply layer ChatGPT onto existing tools. When everything is built on one platform with consistent security protocols, dealmakers can move faster without compromising data integrity.

Generic Or Purpose-Built

The AI integration wave is maturing rapidly. The firms that will win are those that move beyond simple productivity gains to build genuinely secure, purpose-built AI systems that enhance decision-making without compromising data integrity.

This means demanding more from AI vendors than just impressive demos, and asking hard questions about data handling, security protocols, and legal compliance. It also means choosing tools built for the high-stakes world of investment deals, not generic products wrapped in AI buzzwords.

In the end, the most valuable AI tools won’t be the ones that claim to do everything, but the ones that can do the right things securely, with transparency, and with the level of precision that serious dealmaking demands.

Ready to move to a secure, purpose-built AI system? Contact us to learn more.