Generative artificial intelligence tools like ChatGPT promise productivity gains, and companies across industries are integrating them into daily workflows. Rapid adoption has created a critical vulnerability that many midmarket businesses overlook. Without proper security measures, these platforms can expose sensitive data and create costly legal liabilities that threaten business stability.
Unregulated AI usage threatens midmarket companies
Workplace use of generative AI has grown quickly, with 64 percent of businesses expecting it to boost productivity across departments from marketing to operations. In fact, a survey found that 68 percent of employees use AI tools without informing their managers. The practice has earned the label “shadow AI” because it operates outside the view of IT departments and security teams.
Shadow AI creates immediate vulnerabilities for midmarket companies. Employees who paste proprietary code or customer information into public AI platforms may expose that data to third-party services.
Consumer versions of platforms like ChatGPT may use conversation data for model training unless users opt out, meaning confidential company information could be used to improve models when employees use personal accounts for work. Business and enterprise products typically exclude user data from training by default, but many employees use free consumer versions without understanding the distinction.
The compliance implications go beyond data exposure. Organizations that process personal information are encouraged to maintain contractual data processing agreements with any third party that touches that data. Employees who use unauthorized AI tools to analyze customer records or worker information may create regulatory violations that can result in substantial fines and legal action.
Check Point discovers shadow AI across workflows
Midmarket businesses often struggle to identify which AI tools employees use across their organization. Check Point Workforce AI Security addresses the discovery problem by mapping every AI interaction across browsers, desktop applications, and coding assistants. The platform deploys through a lightweight browser extension that requires no infrastructure changes.
Once deployed, the system catalogs both sanctioned and unsanctioned AI tools while monitoring how employees interact with platforms such as ChatGPT, Claude, and GitHub Copilot. Check Point uses contextual analysis to detect sensitive information in prompts before data reaches external AI providers. When an employee attempts to paste source code or customer records into a prompt, the platform can redact, block, or flag the action based on predefined policies.
For midmarket organizations, one attraction is the relatively quick deployment. IT teams gain visibility into AI usage patterns on day one without disrupting employee workflows or requiring changes to complex security architectures.
How Darktrace secures enterprise AI operations
Monitoring AI behavior across networks is possible without disrupting daily operations when security solutions establish baselines of normal activity. Cybersecurity company Darktrace builds a profile for every user and device by observing how employees typically interact with applications, what data they access, and when unusual patterns emerge.
When a marketing team member accesses AI tools during business hours to draft social media content, the multilayered AI technique registers the behavior as normal. An alert is triggered if the same employee uploads customer databases to an external AI platform at 2 a.m., as the action deviates from established patterns.
Generative AI tools give employees easier access to organizational data than traditional software did. Well-intentioned workers can accidentally leak or access restricted information through these platforms. Darktrace learned in 2023 that approximately 50 percent of its customers’ staff had been using generative AI services, highlighting the need for strong data governance.
By investigating every alert and determining whether it is part of a wider security incident, Darktrace helps reduce the number of critical incidents that require human attention. Security teams can then focus their expertise on validated incidents instead of chasing false positives.
Forcepoint enforces compliance through ChatGPT monitoring
Forcepoint GenAI Security integrates with OpenAI’s ChatGPT Enterprise Compliance API to monitor data interactions within the ChatGPT environment. The platform combines cloud access security broker capabilities with information security posture management.
It analyzes user queries to identify potential compliance breaches and assigns risk scores based on information sensitivity. When employees query AI systems with regulated data types, the platform tracks the flow of information and can enforce geographic restrictions to meet regional compliance requirements. The system maps how information moves between users, AI platforms, and internal data stores.
For businesses navigating GDPR, HIPAA, or industry-specific regulations, Forcepoint provides the audit trails and policy enforcement needed to demonstrate compliance. The platform prevents well-intentioned employees from creating regulatory violations by feeding protected information into AI tools without realizing the compliance implications.
Samsung’s data leaks expose the cost of unmonitored AI use
The risks of unmonitored AI adoption become concrete when examining what happened at Samsung. Employees began pasting sensitive source code and confidential meeting notes into ChatGPT to speed up their work. The company discovered the data leaks only after proprietary information had already been exposed on a third-party platform. Samsung responded by banning all employee access to ChatGPT and similar generative AI tools across the organization.
The blanket ban created its own problems. Samsung engineers worked under restrictions that may have limited productivity as other organizations continued exploring AI-assisted development. The company eventually reversed course and began fully adopting generative AI models after implementing security controls that prevent unauthorized data exposure.
Samsung’s experience offers a clear lesson for midmarket businesses. Discovering data leaks after they occur forces reactive decisions that can set organizations back months. The alternative requires a security infrastructure that monitors AI use from the start, rather than policies that either ignore the risks or ban AI tools entirely after damage occurs.
Securing operations ensures sustainable growth
Businesses that secure their AI infrastructure today protect the competitive advantage these tools provide tomorrow. Organizations adopting AI at scale increasingly need security controls that match how employees actually use these tools. Organizations that implement proper safeguards can innovate confidently, while those that ignore these risks may face data breaches and regulatory penalties that erode customer trust built over the years.
Zac Amos is the Features Editor at ReHack, where he covers business tech, HR, and cybersecurity. He is also a regular contributor at AllBusiness, TalentCulture, and VentureBeat. For more of his work, follow him on X (Twitter) or LinkedIn.
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