Why organisations need to secure AI use, not ban it
By Raymond Schippers (pictured), Lead Technologist – A&NZ at Check Point Software Technologies
The velocity of change in Artificial Intelligence (AI) is testing the responsiveness of corporate Australia. New tools emerge weekly, capabilities expand monthly, and use cases evolve faster than most governance frameworks can be drafted.
For many executives, the instinctive response has been to slow things down restricting usage or banning Generative AI outright. Yet there is growing evidence that suggests this approach is not only ineffective, but counterproductive.
Employees are not leaking data out of malice. In most cases, they are simply handing it over to AI tools in pursuit of speed, productivity and better outcomes. When official channels are closed, unofficial ones proliferate.
Shadow AI, much like shadow IT before it, thrives in environments where controls lag behind reality. The result is greater risk, not less risk.
A growing number of organisations are therefore rethinking their stance. Rather than asking how to stop AI, they are asking how to manage it. The most effective responses share a common structure: a three-phase approach built around discovery, protection, and governance.
Phase One: Discover what is already happening
The first mistake many organisations make is assuming they know how AI is being used within their walls. In practice, few do. Discovery is about developing a clear-eyed view of reality: which tools employees are using, how frequently, and under what accounts.
Are staff relying on personal logins for public AI platforms, or are they using approved enterprise tools? Are teams experimenting independently, or are there pockets of sophisticated adoption already delivering value? Without this visibility, leadership is effectively flying blind.
Discovery also exposes a second, often uncomfortable truth. Many organisations have invested heavily in AI tools yet cannot clearly articulate the return on that investment.
Licences are purchased, pilots launched, but usage patterns remain opaque. Understanding where AI is genuinely improving productivity – and where it is simply adding noise – is a prerequisite for any credible strategy.
Importantly, discovery should not be punitive. The objective is not to catch employees out, but to learn from behaviour that is already pointing to where value lies.
Phase Two: Protect data and the business context
Once usage is visible, the next challenge is protection. Not all data is equal, and not all AI interactions carry the same risk. The central question is context: what kind of data is being uploaded, and for what purpose?
Business-sensitive information, such as commercial data, intellectual property, and customer records, demands a different level of scrutiny than generic or personal content. Without contextual awareness, organisations cannot meaningfully assess risk.
This is where blunt controls fail, because they treat all AI usage as equally dangerous, when it is clearly not.
Protection also cuts both ways. AI is not only a productivity tool but is also increasingly a weapon. Adversaries are using AI to craft more convincing phishing attacks, automate reconnaissance, and probe systems at scale. Monitoring for AI-enabled threats is now part of the defensive baseline, not an advanced capability.
In this sense, AI risk management is not solely about preventing data loss. It is about understanding how AI reshapes the threat landscape and adjusting controls accordingly.
Phase Three: Govern, don’t block
The final phase, governance, is where strategy becomes sustainable. Blocking AI may appear decisive, but it ignores human behaviour and market reality.
Employees will continue to seek out tools that make them more effective. Governance accepts this and focuses instead on guardrails.
These guardrails define what “good” looks like: which tools are approved, what data can be used, under what conditions, and with which accountability. Clear policies, combined with training, are far more effective than technical prohibitions alone. When employees understand how to use AI safely and productively, compliance improves organically.
Crucially, governance cannot be static as AI technologies evolve too quickly for once-a-year policy updates. Ongoing risk assessments are essential, as are mechanisms to update controls in line with new capabilities, threats and regulatory expectations. Governance, in this context, is a living system, not a rulebook gathering dust.
From fear to maturity
What this three-phase approach ultimately represents is a shift in mindset. AI adoption is not a binary choice between innovation and risk. It is an operational challenge that demands the same discipline applied to cloud computing, cybersecurity and data governance over the past decade.
As organisations move from experimenting with AI to embedding it into everyday workflows, the real challenge is no longer whether AI should be allowed, but how it can be used safely, visibly, and at scale. Blocking AI tools outright often pushes usage underground, increasing risk rather than reducing it. A more effective approach is enabling AI adoption with built-in guardrails that provide real-time visibility into AI usage, understand data context, and prevent sensitive information from being shared before it leaves the organisation.
This prevention-first approach allows organisations to secure AI without slowing innovation. Key benefits include continuous insight into which AI tools are being used and how, context-aware controls that stop sensitive data leakage in real time, AI-driven threat intelligence that adapts to emerging attack techniques, and centralised governance across users, devices, and locations. By securing AI interactions at the point of use, organisations can shift from fear-based restrictions to confident, responsible adoption—turning AI from a risk into a strategic advantage.
The alternative, which is blanket bans or reactive controls, may offer temporary comfort. But in a world where AI capability is increasingly embedded in everyday tools, comfort is not a strategy. Adaptation is a much better approach.
