A recent Axis Capital survey revealed a dangerous disconnect. CEOs see AI as a productivity engine. Security leaders see it as a data leak waiting to happen.
This is not a communication issue. It is a governance issue and it is leaving organisations exposed at exactly the moment they are trying to scale AI.
“AI fails when productivity and security are measured separately. Until organisations govern AI against a single value model, it will always feel risky to one side and restrictive to the other.”
-Luke Elston, Microsoft Practice Dieector, CyberOne
The data is clear. CISOs ranked shadow AI as their number one risk at 27.2%. CEOs ranked data leakage as their top AI-related threat at 28.7%, compared to just 17.2% of CISOs. Leaders are optimising for different failure modes and running AI to different scorecards. [Axis Capital Survey]
One side is pushing for speed and value. The other is trying to prevent incidents. Both positions are rational. The problem is that AI is being deployed without a shared operating model that links productivity to protection.
The Unit of Value Problem
At board level, AI is framed as an upside story. The CEO measures success in output per pound, faster cycle times and time-to-value. The assumption is that risk can be managed as adoption accelerates.
Security leaders see a different equation. Their unit of value is risk reduced per control and provable compliance. The assumption is that a single uncontrolled data exposure can wipe out any productivity gain.
Both views are correct. The failure is treating them as competing priorities rather than two sides of the same value equation.
The CEO asks, “How quickly can we scale AI to unlock growth?”
The Security Lead asks, “How tightly do we need to govern this to limit blast radius and prove control?”
Until those questions are answered together, AI adoption becomes unstable.
When AI Moves Faster Than Governance
When AI is rolled out without governance keeping pace, the pattern is predictable.
Microsoft Copilot is enabled to drive productivity. Teams experiment with external AI tools to move faster. Data classification is partial. SharePoint and Teams permissions are broader than anyone realises. AI-specific DLP is either missing or not tuned for prompts and outputs. Audit data exists, but it is not designed to support rapid, executive decisions.
Nothing looks reckless in isolation. The risk sits in the gaps.
Sensitive commercial information starts to flow through AI tools in ways the organisation did not intend. Content that was accessible, but never meant to be combined or repeated, appears in AI-generated outputs. One message, document or email can trigger legal, commercial or regulatory consequences.
The impact is rarely dramatic, but it is always disruptive.
Incident response activity spikes. Legal and compliance reviews follow. Sales activity slows as approvals tighten. AI rollouts pause while controls are retrofitted. Momentum is lost and confidence drops.
The cost compounds quickly through direct spend, margin pressure and delayed value realisation.
This is not theoretical. IBM research shows that 97% of AI-related security breaches involve systems without proper access controls. Shadow AI incidents cost on average £670,000 more than traditional breaches.
The issue is not that AI exists. The issue is scaling AI without governance designed for Microsoft-native AI workloads.
Turning AI Into a Controlled Value Engine
High-performing organisations solve this by making productivity and protection the same conversation.
“AI governance only works when protection becomes an enabler, not a brake. In Microsoft-native environments, the organisations that move fastest are the ones that treat data labelling and access control as the licence to scale AI, not the price you pay after something goes wrong.”
-Luke Elston, Microsoft Practice Director, CyberOne
That starts with shared metrics that both business and security leaders recognise as indicators of value.
The most effective AI programmes track:
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Time to productivity versus time to control
How quickly AI can be enabled safely, not just switched on. -
Percentage of sensitive data labelled and governed
The leading indicator of how much AI can be safely scaled. -
DLP incidents per 1,000 AI prompts
A practical measure of control noise and usability. -
Mean time to revoke or quarantine risky outputs
The true size of the blast radius. -
Auditability of who accessed what, when and why
The evidence trail boards expect.
These metrics only work when they are anchored in the Microsoft Security ecosystem organisations already use.
At CyberOne, we see the strongest outcomes when AI governance is built on:
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Microsoft Purview for data classification, DLP and AI safety tied to sensitivity labels
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Microsoft Entra for identity, Conditional Access and step-up controls
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Microsoft Defender XDR for detection and response across endpoints, email and SaaS
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Microsoft Sentinel for unified monitoring, reporting and evidence
This approach reframes AI as a controlled value engine. You scale where data is labelled and governed. You ring-fence what is not. You prove control continuously.
A Four-Week Path to Safe Productivity
This does not require a long transformation programme.
The most successful organisations focus first on reducing blast radius quickly while keeping productivity moving. A four-week stabilisation cycle is often enough.
Week 1: Decide & Discover
An executive sponsor is named. Success metrics are agreed. A practical label taxonomy is approved. Priority data locations are identified. Overshared sites and risky connectors are surfaced. Audit gaps are closed.
Week 2: Configure & Pilot
Labels are created and piloted. Baseline DLP is enabled. Copilot grounding is restricted to governed content. Session controls are applied to external AI tools. Initial user rings are enabled.
Week 3: Enforce & Tune
Key DLP rules move from alert to block or justify. Oversharing is reduced. High-value sites are onboarded into governed libraries. Conditional Access is applied to sensitive actions. Wider rollout begins.
Week 4: Operationalise
Exception workflows are embedded. Dashboards are handed over. Runbooks are documented. A backlog is agreed for broader labelling and third-party applications.
The goal is safe productivity now, not perfect governance later.
The Metric That Aligns Leadership
One metric consistently aligns boards, executives and security leaders.
The percentage of priority sites and mailboxes that are labelled and governed.
For Business Leaders: Coverage represents scale capacity. Each increase is a licence to expand AI access with confidence.
For Security Leaders: Coverage means fewer unknowns, fewer exceptions and stronger policy inheritance.
For both, it creates a simple rule for rollout decisions. Expansion happens when coverage reaches the agreed threshold.
Coverage becomes the throttle for AI adoption.
When There Is No Full-Time CISO
Many organisations operate without a full-time CISO. The model still works. Authority is designed into policy rather than tied to individuals.
A business sponsor, often the COO or CFO, owns safe productivity. Day-to-day decisions are delegated to a Security Authority Proxy with clear decision boundaries. Cadence replaces constant presence through short daily triage, weekly risk reviews and monthly executive checkpoints.
Microsoft-native evidence replaces opinion, allowing fractional security leadership to operate effectively.
Holding the Line Under Pressure
Competitive pressure will arrive before governance feels complete.
When it does, disciplined organisations respond with facts, not emotion. Coverage trends, DLP noise, time to unblock and known risk hotspots drive decisions. Leaders choose between holding briefly to hit thresholds or accelerating safely within narrower guardrails.
Because stop conditions are pre-agreed, momentum is protected and trust is maintained.
Closing the Gap
The CEO–security perception gap on AI is not about awareness. It is about governance.
You close it with shared metrics, Microsoft-native controls and a coverage-led rollout model that links protection directly to productivity.
The fastest path to value is not bypassing controls. It is expanding AI only where data is governed.
Coverage is the throttle.
How CyberOne Helps
At CyberOne, we help organisations use Microsoft AI to drive productivity without introducing unnecessary risk.
We support customers in putting practical governance around AI using Microsoft Security, focusing on outcomes rather than theory. This typically includes:
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Establishing clear governance and data protection foundations aligned to Microsoft Purview and Entra
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Improving visibility and reporting using Microsoft Sentinel so leaders can see coverage, risk and response clearly
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Designing and tuning data protection controls that work with AI prompts and AI-generated outputs
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Providing security leadership support to help organisations make decisions, manage exceptions and keep momentum
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Coaching sponsors and Heads of IT so the operating model embeds and scales over time
Our approach is designed to help organisations move quickly where data is governed, slow down where it is not and make informed decisions with evidence.
If you want to scale AI without creating new risk, start with governance that enables performance. Book a 30-minute review to discuss where to begin.