Enterprises are racing to adopt generative artificial intelligence (AI) to stay competitive in their markets. However, security teams face a significant challenge because they are relying on outdated frameworks designed for a different technological era. The tools that have successfully protected organizations for decades now leave critical vulnerabilities exposed when employees interact with browser-based AI platforms. To safely adopt the latest technology, businesses must completely rethink how they control access to information and detect emerging threats.
The Growing Threat of Shadow AI in the Enterprise
Shadow AI emerges when employees bypass official IT channels and use unauthorized consumer-grade AI tools for work tasks. These platforms offer speed and convenience that internal approval processes cannot match, making them irresistible to productivity-focused workers.
Recent research reveals that 73.8% of employee engagement with ChatGPT occurs on noncorporate accounts, meaning sensitive information flows directly into public models. Likewise, usage of Gemini and Bard on personal accounts reaches 94.4% and 95.9%, respectively.
Outsourcing tasks to unvetted external platforms carries substantial consequences. Analysis shows that 55% of all AI failures stem from these third-party solutions, creating risks that span reputational damage and financial losses. Consumer distrust, compliance penalties and litigation often follow. For companies that use third-party AI tools without conducting risk analysis before deployment, this is alarming.
Without enforceable guidelines, employees overshare sensitive company information, creating massive privacy liabilities, while many third-party platforms lack the rigorous security measures needed to protect confidential business records. Storage policies and practices across these solutions can inadvertently expose customer information and financial records to potential breaches.
Why Traditional Data Loss Prevention Fails
Legacy data loss prevention (DLP) tools were engineered to stop files from being downloaded or sent via email attachments. These solutions excel at their original purpose but face a fundamental limitation in the age of browser-based AI. For example, they cannot easily monitor or block text that an employee manually types or pastes directly into an AI prompt window, creating a blind spot in security coverage that grows more problematic with each passing quarter.
The gap becomes clear when examining what these tools can and cannot protect:
- DLP catches: File downloads, email attachments, USB transfers and document uploads
- DLP misses: Copy-paste actions into browser windows, manually typed prompts, screenshots converted to text and direct text entry into web applications
As AI adoption accelerates across organizations, this coverage gap widens and generates an avenue for information exfiltration that traditional security infrastructure was never designed to address. Security teams find themselves in a reactive position, discovering breaches only after sensitive content has already left the organization’s control.
What’s the Best Approach for Secure Enterprise AI?
Clinging to legacy DLP solutions while ignoring the reality of AI adoption does not constitute a viable long-term business strategy. Companies need to upgrade to AI-driven threat detection that adapts to novel attacks and enforces strict boundaries on AI usage that protect sensitive information. Centralizing governance practices to prevent accidental exposure completes this security foundation.
Shift to a Multilayered AI Context
Traditional threat detection tools rely on reactive rules that search for historical signatures of known attacks. This approach either generates excessive false positives by flagging normal employee behavior as suspicious or completely misses novel attacks that do not match any previous pattern in its database.
For example, Artesia General Hospital recognized these limitations in protecting its patient care operations and digital ecosystems, leading the organization to deploy Darktrace technology. Operating without any predefined list of threats, the platform learns every device, user and interaction within the hospital’s network to develop an understanding of what normal behavior looks like from the ground up.
The Cyber AI Analyst component investigates alerts using methods similar to human analysts while threading together subtle anomalies that could indicate genuine threats to patient records and hospital operations. The system autonomously examines Artesia’s network threats to determine which alerts represent actual security incidents.
This multilayered AI approach significantly reduces false positives that burden security teams. When going from 100 benign alerts daily to just two or three critical incidents that genuinely require human attention, their focus narrows. Analysts receive precisely the information they need without spending hours on manual investigation.
Enforce Strict Internal AI Usage Policies
Establishing firm boundaries and deploying technical guardrails that control what information can enter public AI models addresses the vulnerabilities that legacy DLP solutions cannot close on their own.
Samsung provides a cautionary example of what happens when employees access AI tools without adequate technical safeguards. Within just 20 days after allowing ChatGPT access in April 2023, the company experienced three separate leaks that compromised highly confidential information across multiple departments.
Engineers pasted proprietary semiconductor database source code into ChatGPT to check for coding errors, revealing critical details about Samsung’s manufacturing processes. In another incident, an employee uploaded specialized code designed to identify equipment defects while seeking optimization suggestions. Staff also converted recorded internal meetings to text before feeding those transcripts to ChatGPT for automatic minute generation.
Once information enters a public AI model’s training dataset, there is no delete button to retrieve or scrub it from the knowledge base. Samsung ultimately banned ChatGPT entirely because leaked proprietary content cannot be recovered.
Establish Centralized Data Governance
An enterprise AI solution is only as secure as the information it can access within the organization, and standardizing permissions across all systems forms the foundation of secure AI deployment. When a company’s internal network suffers from inconsistent access controls and fragmented management practices, even approved AI tools can accidentally surface confidential HR files or financial records to unauthorized employees.
For example, AXIS Capital confronted this challenge directly when dealing with stand-alone, siloed policy and claims platforms scattered across different geographies. Each business line had created its own reporting reference standards, and the inconsistencies led to errors in quoting, underwriting and claims adjustments that made corporate-level reporting extremely difficult.
The insurance company developed an organization-wide strategy utilizing web-based stewardship forms and strict security rules to standardize core reference hierarchies. Starting with the North American Industry Classification System and Standard Industrial Classification codes, AXIS then expanded into rating and underwriting codes that had previously varied by location.
By implementing centralized data management and control, the company created a single secure source of truth accessible across all operations. This eliminated inconsistencies that had plagued their platforms while allowing AXIS to accelerate core insurance processes such as introducing new product offerings, reducing operational risk and achieving reliable analytics at the corporate level.
Securing the Future of Enterprise Innovation
AI adoption in business is inevitable as organizations compete for market advantage. Relying on legacy security frameworks and tolerating unstructured governance practices will only accelerate leaks and compliance violations. The question is not whether companies will adopt AI, but whether they will do so securely. By implementing multilayered AI threat detection and establishing rigorous oversight of information access, businesses can embrace AI innovation while protecting their most sensitive content.
