How to Deploy Generative AI Securely: The Strategy for Confident Adoption

Generative AI is transforming how organisations operate. It boosts productivity, accelerates decision making and enables entirely new ways of working. Yet most leadership teams face the same challenge. They see the potential, but still feel unsure about how to adopt AI securely, responsibly and at pace.

At Tenzing, we focus on helping organisations navigate this journey. Across sectors, maturity levels and operating models, the patterns are remarkably consistent. Organisations want to embrace AI, but they lack clarity on the risks, the controls and the practical steps required to move forward confidently. Based on this experience, we developed our AI Security Maturity Model to help leaders understand where their organisation stands and what good looks like.

Our AI Security Maturity Model helps organisations benchmark their current level of AI readiness across four stages of maturity. It highlights strengths, risks and the guardrails needed for safe scale. Leaders can use this model to determine where they are today and define a clear, achievable roadmap forward.

The good news is secure AI adoption does not require a long or complex transformation. It starts with understanding where risks live today and focusing on the fundamentals that unlock value safely. Across our client work, three areas consistently determine whether AI becomes an accelerator or a liability.

AI Guardrails: Protecting What Matters Most

Generative AI does not create new risks. It amplifies weaknesses that already exist. If sensitive data is poorly governed, AI will expose it faster. If access rights are loose, AI will surface insights to the wrong people. If personal devices are unmanaged, AI increases the risk of data leaving the organisation.

This is why strong AI Guardrails are essential before scaling AI. They prevent:

• Sensitive data leaking through prompts or outputs
• Employees accessing information beyond their role
• Data moving into unmanaged personal apps or devices
• AI generating outputs that include confidential insights because data was never classified or labelled correctly

Every breach we see in the market stems from issues that were already there, just not visible. Just take Samsung Electronics as an example. In 2023, sensitive meeting notes and source code were leaked from Samsung — not through malicious activity, but through the use of ChatGPT. Samsung lacked appropriate guardrails to prevent sensitive data being used in Shadow AI tools and paid the price.

Observability & Risk: Creating Visibility Before You Scale

One of the most important insights from our client work is simple. You cannot secure what you cannot see.

Shadow AI is already widespread. Employees use AI tools to solve problems, speed up work or experiment with new ideas. Without visibility, leaders lack clarity on what data is being shared, which tools are in use and where the highest risks sit.

Without this visibility, it becomes impossible to answer key leadership questions:

• What AI tools are being used across the organisation?
• What data is flowing into them?
• Which users or teams present the highest exposure?
• Where are the immediate risks that need action?

The risk is not the AI itself. It is the blind spots created by unmonitored usage.

Operating Model: Aligning People, Process and Technology

Technology brings a lot to the party, but technology alone cannot deliver secure AI. People, culture and clarity determine success.

Effective governance is not a set of static policies. It is a leadership framework that aligns expectations, defines accountability and supports teams with clear guardrails. It is the difference between employees feeling empowered to use AI safely and employees experimenting without understanding the consequences.

Osborne Clarke have been a shining light in this regard. Osborne Clarke announced in the summer of 2025, that it was establishing an international AI Management Board. The objective of this was simple. Assemble a team of experts across the business to establish a shared understanding of the firms AI strategy, rather than it being an IT-led project.

Organisations that adopt AI successfully invest in three things:

• Clear policies that define acceptable use and reduce ambiguity
• Shared accountability across security, IT, legal, compliance and business leadership
• Technology that enables employees to unlock the benefits of AI while remaining secure

Many organisations misstep here. They publish policies but fail to operationalise them. They underestimate the need for communication and training. They forget that trust grows when people understand not only what the rules are, but why they exist.

A Practical Path Forward

Blocking AI outright is no longer sustainable. It slows innovation, frustrates employees and increases shadow behaviour. Equally, moving fast without structure creates unnecessary exposure.

The organisations that succeed take a balanced, pragmatic approach:

• They start by understanding their current level of exposure.
• They address the highest risks with practical, achievable guardrails.
• They enable teams with approved, secure AI tools and clear expectations.
• From there, they scale with confidence.

Generative AI can transform productivity, culture and decision making. Secure adoption does not happen by accident. It starts with clarity on where you are today.