Enhancing Enterprise Security for AI Tools: Protecting Data with Skyhigh AI Solutions

As artificial intelligence rapidly reshapes enterprise productivity and workplace routines, the lines between powerful digital assistance and new security risk are being redrawn—forcing organizations to balance productivity gains against an entirely new class of data exposure and governance challenges. Skyhigh Security’s latest expansion of its Skyhigh AI suite, which now delivers targeted protections for Microsoft Copilot and ChatGPT Enterprise, is an explicit response to this emerging tension. The company’s move underscores how critical the convergence of AI-driven workflows and comprehensive security controls has become for enterprises navigating the current threat landscape.

The Rise of Generative AI in the Enterprise: Opportunity Meets Risk​

It’s undeniable: Generative AI tools like Microsoft Copilot and ChatGPT Enterprise are rapidly transforming how organizations create, analyze, and interact with business data. By embedding AI directly within productivity suites—Microsoft 365, Teams, Outlook, and more—enterprises can automate repetitive tasks, accelerate content creation, and conduct advanced research with unprecedented speed. These tools, fuelled by GPT-4-turbo and similar large language models, are fine-tuned for professional scenarios. They streamline email drafting, aid data analysis in Excel, summarize meeting notes in real time, and even suggest strategic insights by mining vast organizational datasets.
But the very integration that makes these platforms so powerful also introduces novel risks. According to Skyhigh Security’s 2025 Cloud Adoption and Risk Report, 11% of files uploaded to AI applications contain sensitive corporate content, while less than 10% of enterprises have implemented robust data protection controls on these data streams. Such statistics paint a stark picture: organizations are embracing generative AI faster than they are securing it.

What’s at Stake: Data Exfiltration, Compliance, and Loss of Control​

AI systems’ hunger for data—feeding user queries, chat contexts, and uploaded documents to large language models—creates fertile ground for accidental data sprawl. When employees use Microsoft Copilot and ChatGPT to brainstorm, summarize reports, or answer customer queries, it’s easy to overlook which snippets or full documents are being sent for processing. The consequences range from inadvertent disclosure of intellectual property to flagrant violations of compliance regimes such as GDPR, HIPAA, or industry-specific mandates.
Skyhigh Security highlights “data ingestion and exfiltration” as major risks: once sensitive data is uploaded, organizations often lose insight and control over how information is stored, referenced, or potentially persisted by third-party AI systems. The lack of standardized processes for monitoring, auditing, and controlling these flows means that even organizations with strong legacy data loss prevention (DLP) programs are left exposed.
Furthermore, AI applications may persist user data for purposes like model fine-tuning, analytics, or future context retention—raising thorny questions around data residency, retention, and sovereignty. Enterprises must grapple with how to honor legal obligations to customers and partners if data flows into opaque or externalized AI environments.

Inside Skyhigh Security’s Purpose-Built AI Protection​

Skyhigh Security’s new solution aims to bridge precisely these gaps. Its offering builds on the company’s Security Service Edge (SSE) platform and leverages deep experience in cloud-native data protection. The product expansion includes tailored safeguards for both Microsoft Copilot for 365 and ChatGPT Enterprise—addressing unique characteristics and workflows in each.

Core Capabilities: How the Solution Works​

• Real-Time Data Scanning and Classification: The solution continuously monitors all data flows between users and AI platforms. Advanced classifiers categorize data according to enterprise sensitivity—tagging intellectual property, personally identifiable information (PII), financials, source code, and other regulated information before it ever leaves the organization.
• Context-Aware Policy Enforcement: Administrators can craft policies restricting or allowing data uploads by content type, user role, or context. For instance, while generic documents might flow freely, confidential HR records or product schematics can be blocked from being sent to Copilot or ChatGPT—even if users attempt to upload them.
• Threat Detection and Response: Integration with incident management platforms ensures that suspicious uploads or patterns—such as “data spraying,” where multiple sensitive files are shared in a short period—are flagged and trigger prompt investigations.
• Granular Logging and Auditing: Every transaction is meticulously logged, giving compliance teams forensic insight into exactly what data was sent, by whom, and which application processed it.
• End-User Education and Just-In-Time Alerts: Skyhigh’s system can present warning banners, require user acknowledgments, or even block submissions at the point of upload if a rule would otherwise be violated, fostering a culture of security mindfulness.

These features position the offering as a comprehensive, proactive shield rather than a simple audit-after-the-fact control. Skyhigh’s tight integrations with Microsoft’s native APIs and OpenAI’s enterprise endpoints are designed to enable policy granularity without compromising the seamless work experience that makes AI tools so attractive in the first place.

Addressing Key Use Cases​

The unique flexibility of Copilot and ChatGPT means that users may interact with these services in myriad ways, ranging from summarizing internal project updates to analyzing external market intelligence. Skyhigh Security’s policies can be tailored to specific departments, use cases, device types, and data classifications. That means finance teams might have one set of controls (permitting financial modeling), while R&D is restricted (blocking product designs from leaving internal boundaries).
Specific risk scenarios Skyhigh targets include:

• Copy-paste leakage: Preventing users from inadvertently copying sensitive paragraphs or tables into a generative AI prompt.
• Bulk uploads: Throttling or outright denying uploads of multi-document archives containing customer lists or trade secrets.
• Shadow IT AI use: Detecting and controlling unauthorized (unsanctioned) AI bots or browser extensions employees may attempt to use for work tasks.

By combining traditional DLP methods with AI-specific context and monitoring, the platform aims to create a unified framework where old and new risks are treated with equal rigor.

Industry Context: Why Purpose-Built AI Security Matters​

Skyhigh Security’s expansion into AI risk mitigation is neither unexpected nor isolated. It’s part of a larger trend where data protection specialists are racing to adapt their offerings for an AI-saturated workplace.

Established Players Respond to the AI Wave​

Microsoft, for its part, has emphasized that Copilot for Microsoft 365 is built atop its highly compliant Azure cloud infrastructure. The company touts adherence to global standards such as ISO/IEC 27001, FedRAMP, and industry vertical regulations, and publicizes features like customer data isolation and exclusion of customer data from AI model training by default in enterprise licenses. Enterprise security reviews confirm that Microsoft’s governance tools (such as granular permissions and end-to-end encrypted session handling) are robust and evolving in response to customer needs.
Still, real-world security experts caution that compliance doesn’t always translate into comprehensive protection—especially in fast-evolving scenarios where user behaviors, new workflows, and the inexorable pace of generative AI advancement quickly outstrip legacy policy frameworks and user training.
By providing independent, adaptive controls at the data layer, vendors like Skyhigh augment the native protections offered by platform giants, closing gaps where rapid AI adoption may outpace IT oversight.

A View From the Front Lines: What Enterprises Experience​

Real-world adoption rates, as revealed by Skyhigh’s own cloud risk assessment, indicate that employees—even in highly regulated sectors—are using generative AI at a brisk pace. The report’s finding that less than 10% of enterprises have implemented enforceable data loss controls for AI use is corroborated by anecdotal evidence from IT consultancies and CISO roundtables. The appeal of enhanced productivity is causing some organizations to “look the other way,” while more cautious organizations slow AI rollout due to fears of a breach or compliance incident.
Skyhigh’s EVP of Product, Thyaga Vasudevan, summed up the problem succinctly: “Once sensitive data is shared with these chatbots, organizations lose control of what happens to that data.” The company’s approach is to “give enterprises the proper tools and controls to capture AI’s full value while maintaining control over their data.”

Critical Analysis: Strengths and Potential Gaps​

Strengths​

• Purpose-Built for AI: Skyhigh’s offering is not a retrofitted legacy solution—it is expressly tailored for the unique workflow and risk profile of modern generative AI platforms.
• Deep Integration: By leveraging partner APIs and close collaboration with Microsoft, the controls are both fine-grained and minimally invasive—crucial for reducing friction in user workflows.
• Holistic Protection: By offering real-time scanning, context-aware rules, and incident response, the solution doesn’t just flag issues; it helps prevent them and automate remediation.
• Scalability: The cloud-native architecture supports rapid deployment across even the largest, most distributed enterprises.
• Audit and Compliance: Detailed forensic logging and proactive compliance reporting help organizations navigate audits and regulatory inquiries with confidence.

Potential Risks and Limitations​

Despite its innovations, no solution is without trade-offs or unresolved questions:

• Complexity of Deployment: As with all advanced enterprise security platforms, initial setup—defining policies, integrating with diverse data sources, and tuning for minimal false positives—may require concerted effort, change management, and user training.
• User Pushback: Overly restrictive policies or excessive notifications could frustrate users, pushing them to circumvent controls or shift to unmonitored shadow IT tools.
• Emergent AI Risks: AI models and platforms evolve quickly, sometimes introducing new features or third-party integrations that may outpace Skyhigh’s ability to monitor and control them in real time. Continuous vigilance and rapid iteration are essential.
• Vendor Lock-In: Relying on a specific security stack for AI data protection may hinder flexibility if organizations decide to migrate between AI providers or adopt additional AI-powered productivity tools outside the Microsoft/OpenAI ecosystem.

Counterpoint: Are Native Controls Enough?​

There is ongoing debate within the security community about whether third-party overlays like Skyhigh’s are necessary for organizations that have robust, well-configured AI platform controls and a strong security culture. Proponents of relying on platform-native features argue that, with proper configuration and policy enforcement, much of the risk can be mitigated without additional investment.
However, in practice, the speed with which users adopt new tools, coupled with the limitations of vendor-native DLP (especially in the rapidly changing world of generative AI), means that most enterprises require a layered approach. Independent controls improve visibility, enforce corporate policy across hybrid and multi-cloud environments, and often include advanced analytics or machine learning not yet available out-of-the-box from platform providers.

The Bigger Picture: What Comes Next?​

Skyhigh Security’s expansion into AI data protection signals an industry-wide recalibration toward “secure productivity.” As natural language interfaces and chat-based assistants become the default mode of enterprise interaction with data, the need for dynamic, context-sensitive security is only going to intensify.
Regulatory scrutiny is also increasing. Policymakers and consumers alike are demanding greater assurances that corporate and personal data will not be inadvertently exposed, ingested, or repurposed for unintended uses by advanced AI systems. Solutions that provide continuous, adaptive enforcement—and can demonstrably prove their value in audits—will likely become standard requirements for enterprise IT.
Meanwhile, savvy organizations will continue to blend policy, process, and best-in-class technology. End-user empowerment, continuous monitoring, and explicit auditability are essential pillars—not just for compliance, but for building lasting trust in generative AI within and beyond the corporate firewall.

Conclusion​

Skyhigh Security’s purpose-built solution comes at a pivotal moment for enterprise AI adoption. By directly targeting the novel (and very real) risks that tools like Microsoft Copilot and ChatGPT Enterprise introduce, it offers a credible framework for organizations seeking to maximize the benefits of generative AI while minimizing compliance, privacy, and reputational pitfalls.
Yet, as is so often the case in security, there is no silver bullet. It is up to each organization to rigorously assess its risk profile, tailor policies to unique business needs, and remain vigilant as the landscape evolves. At the intersection of secure productivity and responsible AI, those who proactively adapt today are most likely to thrive tomorrow amid rapid technological, regulatory, and threat-driven change.

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Source: Yahoo Finance https://finance.yahoo.com/news/skyhigh-security-launches-purpose-built-130000884.html