AI and Security: How to Protect Corporate Data and Reduce Risk

AI is already inside your business. Your teams are using it every day, often without oversight or control. At this point, you only have two choices govern it or let it govern you.

Discover how to safeguard your data, processes and compliance. Turn AI from a liability into a competitive advantage.

68% of employees who use generative AI at work access it through personal accounts, outside IT control
57% have entered sensitive data into public platforms, raising the risk of Shadow AI
84% want to keep using AI at work because it improves speed, creativity and productivity

(Source: TELUS Digital, AI at Work survey, January 2025, 1,000 U.S. enterprise employees)

The point isn't that employees use AI. It's that every document pasted into a public chatbot leaves your control and stays readable, outside your perimeter, for good. Serious cyber incidents rose 48.7% worldwide between 2024 and 2025, the steepest jump on record, and the Clusit 2026 Report names generative AI in attackers' hands as a key force multiplier.

(Source: Clusit, 2026 Report on Cybersecurity)

Phishing still opens most breaches, and attackers no longer target firewalls, they target people. We looked at where European cybersecurity is heading, from digital sovereignty to post-quantum encryption, in our take on European Cybersecurity Month 2025.

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AI and Security: How to Protect Corporate Data and Reduce Risk

AI is already inside your business. Your teams are using it every day, often without oversight or control. At this point, you only have two choices govern it or let it govern you.

Discover how to safeguard your data, processes and compliance — and turn AI from a liability into a competitive advantage.

68 percent use generative AI with personal accounts
57 percent upload sensitive data to public platforms
84 percent will keep using AI even without policies

(Source: TELUS Digital’s AI at work survey)

From the growing role of AI in cyber attacks to the best practices for adopting it securely, this white paper provides a clear, practical and actionable overview, including:

The evolution of AI-driven threats deepfake, adaptive malware, advanced phishing, RaaS
How leading vendors and cybersecurity players are moving
Where AI delivers measurable value in cyber defense
The real risks of Shadow AI and public AI usage
AI on-premise advantages, limits and impact on security and compliance
Open source vs proprietary models what makes sense, when and why
Best practices for secure AI adoption security by design, data governance, regulations

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WHO IS THIS GUIDE FOR?

A practical resource for organizations adopting AI without exposing data, processes or compliance.

CIO, CISO, IT Security Managers

Close the Shadow AI gap without banning the tools your teams rely on, and decide which AI architecture keeps data under your control.

Risk, Compliance and DPO Teams

For teams responsible for data governance, accountability and alignment with GDPR, NIS2 and DORA.

Executives and Business Owners

For decision-makers who must assess impact, risk exposure and investment priorities for data security.

WHAT ARE THE AI RISKS AND DEFENSES EVERY BUSINESS SHOULD KNOW?

A clear overview of emerging risks, AI-driven attack techniques and defense strategies

New AI-Driven Threats

Deepfakes, advanced phishing, adaptive malware and RaaS. Why traditional defense models are no longer enough

Vendors and Market Trends

How cybersecurity players are responding. Trends, innovations and dynamics shaping investments and security technologies

Shadow AI and Data Exposure

Public AI tools can leak sensitive data and intellectual property: the most underestimated risk for security teams. It happened at Samsung, where confidential data ended up in ChatGPT.

On-Premise AI and Model Choices

Data sovereignty and full control. Pros and cons of on-premise, open source and proprietary solutions

AI in Cyber Defense

Automated detection, incident response, behavioral analytics and anomaly detection. Where AI brings tangible defensive value.

AI Security and Compliance Best Practices

Security by Design, data governance, access control and continuous monitoring. How to adopt AI securely and stay aligned with GDPR, NIS2 and DORA

Bringing AI inside the perimeter

The white paper examines a concrete alternative to public AI assistants: keeping artificial intelligence within the corporate perimeter instead of sending data out to it. This is the logic behind AIGrant, CyberGrant's on-premise private AI: semantic search across internal documents, department-level access segregation, and data that never leaves the organization's infrastructure. It works alongside the productivity tools already in place, closing the gap they leave open: the data itself stays protected, not just access to it.

FAQ

Is using generative AI on corporate documents compatible with GDPR, NIS2 and DORA?

It can be, but not by default. Feeding confidential documents into public AI assistants exposes them to model training, vendor-side storage and third-party access. Compliance with frameworks like GDPR, NIS2 and DORA depends on data governance, access controls and traceability. Keeping AI on-premise means the data never leaves your infrastructure. The white paper breaks down the regulatory requirements and the architectures that satisfy them.

What happens to corporate data when employees use ChatGPT or Copilot for work?

Once data enters a public AI platform, three things can happen: it trains the model, it resurfaces in responses to other users, or it sits on the provider's servers beyond your reach. Source code, strategy, financials and client records are the most exposed, and most employees don't realize they've become the leak. This is Shadow AI, and the white paper details how to close it without banning the tools people rely on.

What are the technical requirements for a private on-premise AI in regulated industries?

A private on-premise AI keeps both models and data on your own infrastructure, with no dependency on public cloud. The core requirements are data sovereignty, department-level access segregation, integration with existing systems, and full traceability of every interaction. The white paper compares on-premise, open source and proprietary models, weighing the trade-offs for security and compliance.

Open source or proprietary AI model for data security? It depends on what you're optimizing for. Open source offers control, customization and independence from any single vendor, but demands in-house expertise to run and maintain. Proprietary models cut the operational load at the cost of transparency and lock-in. For confidential data, the deciding factor isn't the license, it's where the data is actually processed. The white paper compares both within the on-premise context.

Private RAG or Microsoft Copilot Enterprise for confidential documents?

They take opposite approaches. Copilot Enterprise processes data in the Microsoft cloud; a private on-premise RAG system keeps documents and processing inside your own perimeter, with direct control over where information lives. For law firms, funds and regulated sectors, data sovereignty is usually the deciding factor. The two can coexist. The white paper analyzes the governance and compliance implications of each.

How can a business adopt AI securely and stay compliant?

Through security by design: data governance defined upfront, granular access controls, continuous monitoring of interactions, and alignment with regulations like GDPR, NIS2 and DORA from the start. Banning public tools isn't enough on its own; you need to offer a secure internal alternative. The white paper collects the best practices for integrating AI while preserving transparency, accountability and data protection.

Who is the AI & Cybersecurity white paper for?

It's built for CIOs, CISOs and IT security managers deciding on technologies and architectures, for risk, compliance and DPO teams responsible for governance, and for executives weighing risk and investment. It offers a pragmatic, actionable view of AI-driven threats and defense strategies, useful to anyone introducing AI without exposing data and processes.

AI and Security: How to Protect Corporate Data and Reduce Risk

AI and Security: How to Protect Corporate Data and Reduce Risk

WHITEPAPER ARTIFICIAL INTELLIGENCE & CYBERSECURITY

What will you learn in this white paper?

A practical resource for organizations adopting AI without exposing data, processes or compliance.

A clear overview of emerging risks, AI-driven attack techniques and defense strategies

Bringing AI inside the perimeter

FAQ