The Intelligent Control Stack: why AI needs a cybersecurity mindset

“I’m a lawyer and policy expert who has spent years inside cybersecurity functions at sovereign infrastructure scale, including in my doctoral studies. I’ll be blunt: the discipline I trained in is structurally insufficient for adaptive systems, and the gap is widening as deployment accelerates.” 

Betania Allo (AI Governance and Trust Strategist) doesn’t come at AI governance from a single discipline – she builds it from three.

Trained across law, engineering, and international security, her career spans doctoral research in cybersecurity engineering at George Washington University, policy work at Harvard, and legal qualifications across Argentina, Spain, and the US. She has held senior roles at NEOM, advised the UN Security Council and multiple UN bodies, and now leads AI, TMT and Privacy at Legal Tracks in Riyadh, while advising sovereign institutions through her own practice.

So she operates at the level where systems are built, regulated, and deployed – often simultaneously.

We’ve been speaking with Allo about her latest work, the Intelligent Control Stack. It’s a model that reframes AI governance as an operational control problem, not a policy exercise.

But before you read that interview, here’s a quick look at what the Intelligent Control Stack is, and why it’s valuable now. 

A control plane for AI 

Allo’s core idea is that we need to treat AI like any other critical system in cybersecurity.

No CISO would deploy a production workload without logging, monitoring, alerting, and containment. Yet many AI systems (even in critical industries) operate without those controls.

The Intelligent Control Stack is designed to fix that gap. It acts as a sovereign AI control plane – a layer that governs how systems behave under stress, and how organisations respond in real time.

Instead of replacing existing frameworks, this is a way to operationalise them – turning governance from policy into engineering. 

The five layers that make up the stack

At the heart of the model is a five-layer architecture, each addressing a different stage of AI risk. Allo outlined these layers in a recent article for Security Middle East: 

1. Preventive controls

This is the familiar territory: data validation, bias testing, adversarial simulations.

It’s about reducing risk before deployment – but importantly, Allo is clear that prevention alone is not enough.

2. Continuous monitoring

The backbone of the stack.

Here, organisations track model drift, anomalies, and changing decision patterns in real time. The goal is early detection – spotting issues internally before they escalate externally.

3. Containment and escalation

When something goes wrong, speed is critical.

This layer introduces predefined responses: throttling decisions, triggering human override, isolating systems, or reverting to safe states. It mirrors incident response in cybersecurity – fast, structured, and auditable.

4. Adaptive governance

Instead of static policies, governance becomes dynamic here.

Monitoring signals feed back into the system, tightening thresholds or introducing new controls based on real-world behaviour. Governance evolves with the system.

5. Strategic oversight

Finally, the board-level view.

Operational data feeds into executive decision-making and regulatory reporting, creating audit-ready evidence rather than narrative assurances.

A focus on sovereignty

One of the more distinctive aspects of the stack is its focus on sovereignty.

For regions like the Middle East, investing heavily in AI, the questions about capability go hand-in-hand with questions about control. Who owns the data? Who can intervene? 

Allo argues that sovereignty depends on having infrastructure that keeps monitoring, containment, and audit trails within national or institutional boundaries.

Because without that, organisations risk relying on external visibility – and losing control at pivotal moments. 

We’re going deeper

We had questions about the stack – so we’ve been talking to Allo about where traditional cybersecurity models are failing, and how the stack aligns with existing AI governance frameworks. 

For CISOs, this work highlights the reality that AI systems must now be treated as critical workloads (not experimental add-ons). And for regulators, it emphasises that policy alone is no longer enough; enforceability depends on operational evidence.