Security and Generic AI Tools: Why Dealmakers Need AI Built for Finance

Your employees might be leaking your deals to the world right now. If they’re copying pitch decks into ChatGPT to write summaries faster, or pasting financial models into Claude to debug formulas, or even uploading due diligence documents to public artificial intelligence (AI) chatbots they are sharing proprietary information.

This security phenomenon is called “shadow IT”, and it is costing organizations an average of $670,000 in additional breach costs on top of regular security incidents. For dealmakers handling sensitive M&A information, confidential valuations, and proprietary market intelligence, the cost and data shared could be catastrophic.

The uncomfortable truth is that the financial services sector shows the highest awareness of data leak risks at 29% but matches the lowest implementation of technical controls. This presents a clear problem for banks, but the fix isn’t coming in fast enough.

Shadow IT in AI-Powered Dealmaking

Shadow IT isn’t new. Employees have been downloading unauthorized software for decades. But AI changed everything. Microsoft estimates that 78% of AI users now bring their own tools to work, and 60% of IT leaders are concerned that senior executives lack a plan to implement the technology officially.

More than 73% of work-related ChatGPT queries were processed using accounts that were not approved for corporate use. In financial services specifically, 41% of employees use generative AI tools regularly, often without approval, and this number is expected to continue increasing in 2026.

Why does this happen? Because employees are drowning in work and AI tools promise a lifeline. A Capgemini report found that while 46% of software developers are already using generative AI, 63% of those use GenAI unofficially.

Generic consumer tools built for chatting about recipes and homework don’t meet the security standards that financial services demand. The productivity gains are real, but the security risks can be catastrophic.

Generic AI Tools and Security Risks

IBM claims that 20% of organizations last year suffered a breach due to security incidents involving shadow AI. When dealmakers use generic AI tools for sensitive work, three things happen simultaneously.

First, most organizations lack control to detect or prevent employees from uploading confidential data to AI platforms. You don’t know what’s being shared until it’s too late.

Second, sensitive data now makes up 34.8% of employee ChatGPT inputs. The types of data being shared have expanded to include customer information, financial records, intellectual property, and proprietary deal strategies.

Third, compliance violations happen automatically. GDPR requires maintaining records of all processing activities, which becomes impossible when organizations can’t track AI uploads. For dealmakers subject to SOX compliance, AI usage completely bypasses financial data controls when employees paste quarterly results into ChatGPT for analysis.

Samsung banned ChatGPT after its unauthorized use led to sensitive chip designs leaking. This pattern has been repeated across financial services as employees accidentally expose M&A targets, valuation models, and confidential deal structures to public AI systems that may retain, analyze, or repurpose the data.

AI Designed for Finance Industry Security

In February 2025, security researchers discovered a massive vulnerability when over 40 popular browser extensions used by nearly 4 million professionals were compromised. Many were “productivity boosters” that employees had installed to overlay AI functions onto their browsers without IT vetting. Once compromised, these extensions silently scraped data from active browser tabs, including sensitive corporate sessions open in ChatGPT and internal portals.

Organizations using AI and automation extensively slash breach costs to $3.62 million compared to $5.52 million for non-users. AI should make you more secure, not less, when implemented correctly.

The difference is the architecture. Generic AI tools are built for broad consumer use with security as an afterthought. Purpose-built AI platforms are designed with financial services security requirements, audit trails, data residency controls, and compliance frameworks built in the foundation.

Unlike generic AI models that optimize for consumer convenience, Cyndx optimizes for financial services security and compliance. Our platform operates with enterprise-grade encryption, strict data residency controls, comprehensive audit trails, and isolation between client data that prevents cross-contamination.

Best Financial AI for M&A Is Purpose-Built

Cyndx offers an integrated suite of AI deal sourcing software where security isn’t bolted on as an afterthought but engineered into every component from inception.

Scholar, our generative AI solution, is particularly relevant to this discussion. It creates comprehensive research reports from our database of over 32 million companies, any uploaded information, and external resources. The critical difference is that your research queries, target companies, and generated reports remain in Cyndx’s secure environment.
Finder surfaces companies based on capabilities and market activity. Unlike generic search tools, where your queries might reveal your acquisition strategy to competitors, Finder operates within a closed system where your searches remain confidential.
Acquirer identifies acquisition targets based on strategic fit and compatibility. The predictive analytics that flag funding activity and receptiveness operate on our proprietary data infrastructure, not on public systems where your target identification could leak to competitors or the targets themselves.
Raiser pinpoints investors based on actual investment history and deal patterns. When you’re identifying potential buyers or capital sources for a confidential transaction, you need assurance that your search isn’t tipping off the market or competitors about your intentions.
Valer performs sophisticated valuations with adjustable models and comparable analysis. Valuation work is extraordinarily sensitive. Valer processes this information within Cyndx’s secure environment rather than exposing it to generic tools that may not properly protect proprietary valuation methods.

The platform architecture means your entire deal workflow, from initial sourcing through due diligence to valuation, happens within a unified, secure platform. No data leaves the platform.

Generative AI in Finance Requires Financial-Grade Security

Financial services organizations must address the shadow IT problem to safeguard data and maintain compliance. But addressing it shouldn’t mean banning AI. It means providing AI tools that are purpose-built for the finance industry.

In markets where a single leaked deal memo could cost you the transaction, where competitors would pay millions for your target list, and where regulators impose massive fines for data breaches, security in dealmaking isn’t optional. It’s existential.

The shadow IT crisis is accelerating as AI capabilities expand and employees increasingly rely on AI tools. The organizations that will perform better will be the ones that stopped treating AI security as an IT problem and started treating it as a strategic imperative requiring purpose-built solutions.