How to Deploy Generative AI Securely: The 3 Critical Areas Every Organisation Must Assess
Why Securing AI Matters
Generative AI is transforming business operations, enabling automation, creativity, and efficiency at scale. Yet, many organisations face a dilemma: they want to leverage AI's benefits but fear risks such as data leaks, regulatory non-compliance, and unintended access to sensitive information.
A common misconception is getting your organisation ready to use AI securely requires a lengthy 6+ month project and only then can you start using AI meaningfully. In reality, organisations can start with quick wins that enable secure AI adoption while building a strong governance foundation.
The challenge is organisations want to secure their business but don't know where to start and what they are even assessing. This guide simplifies the complexity and outlines the three key areas every organisation must assess before deploying generative AI.
1. Security & Governance Controls: Preventing AI-Driven Risks
Firstly let be clear - Generative AI does not create security risks. It does however amplify and exacerbate existing flaws in your estate in ways we had not considered or prioritised in the past. As such, before deploying AI organisations must ensure they have the right safeguards in place to prevent data exposure, insider risks and data breaches. These risks can derail innovation and damage trust if left unchecked.
Sensitive Data Loss
AI tools can inadvertently expose confidential information through user prompts or generated outputs. Without strong governance, sensitive files could be uploaded to external AI platforms, or internal AI tools could surface data that should remain restricted. This can pose a significant risk as not all AI tools are created equal, with some using users prompts to train their foundational model, surfacing this data elsewhere. This risk became real when the BBC recently reported that nearly 300k Grok chats were accessible through Google searches.
Uncontrolled Access to Corporate Data
Generative AI can make it easier for employees to access information beyond their intended scope. If access controls and data classification aren't enforced, AI becomes a gateway to sensitive information that is not intended for those users.
BYOD and Personal App Exposure
Many organisations have a Bring Your Own Device (BYOD) policy whereby they allow employees to access corporate resources on personal devices. Without the correct controls in place, this creates a risk that a user can transfer sensitive corporate data to personal apps, either through negligence or malice. When AI tools are involved, this creates a significant risk of data leakage outside the organisation's control.
Data Classification and Labelling
AI systems rely on data classification to understand what is sensitive and what is not. If organisations fail to label and protect their data, AI tools cannot distinguish between public and confidential information.
Why This Matters
These risks aren't theoretical - they're already causing real-world breaches. Samsung famously experienced a data leak when employees used ChatGPT without restrictions or the Grok example mentioned previously. Organisations that fail to address these risks face reputational damage, regulatory penalties, and loss of trust.
Key Takeaway
Security controls aren't about slowing innovation, they're about enabling it safely. By addressing these risks upfront, organisations can unlock AI's potential without compromising data integrity or compliance.
2. AI Usage & Risk: Monitoring AI-Driven Risks
Visibility into AI usage is critical. Without it, organisations operate blind to risks such as:
- Shadow AI: Employees using unsanctioned AI tools
- Oversharing Risk: AI enabling access to data beyond a user's intended scope
- High-Risk Users: AI tools being used by malicious users that could damage your organisation
Shadow AI Use
According to Microsoft's Work Trend Index, 75% of employees already use AI at work (often without approval). This means shadow AI is not a future problem; it's a current reality. However many organisations are completely blind to what tools are being used and how they are being used. Without visibility, organisations cannot manage the risks of sensitive data exposure or unethical use.
Oversharing Risks
Generative AI can amplify internal risks by enabling employees to access data they shouldn't access or by aggregating sensitive insights from multiple sources. These scenarios can lead to compliance breaches and reputational harm if not addressed. To address this, organisations need to have a clear understanding of access to sensitive information and high-priority oversharing risks that must be resolved.
Insider Threats and High-Risk Users
AI can be misused by malicious insiders, employees with elevated privileges or perhaps even by attackers who have compromised a users account. Without monitoring and restrictions, these users could exploit AI to extract sensitive data or bypass existing security measures.
Why This Matters
Unmonitored AI usage creates blind spots that can lead to data leaks, compliance failures, and reputational damage. Organisations that fail to address shadow AI risk losing control over sensitive information.
Key Takeaway
Monitoring isn't just about compliance - it's about enabling safe, scalable AI adoption. Organisations that combine visibility with proactive risk management will unlock AI's potential without compromising security.
3. Policy & Governance: Building a Responsible AI Culture
Advanced tools and platforms are essential, but they are not enough. Governance and culture are the real enablers of successful AI adoption. Organisations need clear policies, defined accountability, and a strong change management strategy to ensure AI aligns with ethical principles, regulatory standards, and business objectives.
Why Governance Is More Than Policy
Governance is often viewed as a compliance exercise of writing policies and setting rules. But policy without implementation fails. True governance combines:
- Policy frameworks for trust and compliance
- Ownership and accountability across functions
- Human-centric change management to drive adoption and adherence
Without factoring in the human component with education, communication, and cultural alignment AI initiatives stall. Employees must understand why AI matters, how it impacts their roles, and what safeguards exist. This builds trust and reduces resistance.
Core Elements of AI Governance
- Policy Frameworks: Define acceptable use and compliance.
- Shared Accountability: DPO for privacy, InfoSec for security, IT for implementation, business leaders for ethical use cases, and change managers for adoption.
- Governance Committee: A cross-functional group that reviews use cases, monitors risk, and drives cultural readiness.
Policies and Awareness
Policies define acceptable AI use, reduce shadow AI risks, and build trust. But policies alone aren't enough. Policies fail without people. When employees understand both AI benefits and risks, they become active participants in responsible adoption rather than passive users.
Future-Proofing Governance
Governance must evolve with laws, technology, and culture. Regular reviews, alignment with standards (e.g., ISO/IEC 42001), and ongoing engagement keep AI adoption secure and sustainable.
Bottom line: Governance isn't optional: it's the foundation for secure, ethical, and successful AI implementation.
Why This Matters
Without strong governance, AI adoption risks regulatory breaches, ethical missteps, and failed implementation. Policies alone aren't enough—human adoption and cultural alignment are critical. Governance provides the structure for compliance, security, and trust, while change management ensures employees embrace AI responsibly. Together, they enable organisations to innovate confidently and avoid costly mistakes.
Key Takeaway
AI governance is not a one-time task. It's a living framework that combines policy, accountability, and cultural readiness. Organisations that invest in governance and change management now will lead in secure, ethical, and successful AI adoption.
The Path Forward: Balancing Innovation and Control
Blocking AI outright isn't a sustainable strategy. It limits innovation and frustrates employees who see its potential. A better approach is pragmatic: identify low-risk use cases, provide secure and approved AI tools, and educate teams on responsible usage. This fosters innovation without compromising security.
Generative AI can transform productivity and decision-making, but secure adoption doesn't happen by accident. Start with quick wins, build a strong governance foundation, and empower your teams to innovate responsibly.
To achieve this, organisations need start by understanding where they are today and where they want to be.
If you'd like to assess where your organisation aligned to these three critical areas and define a clear, actionable roadmap for secure adoption, reach out today.