ISJ hears from Richard Dempers, Co-Founder of Rheinberry and Director – Solutions & Technology, and Joe Yaeger, Co-Founder of Rheinberry and Director – Delivery & Professional Services.
Why AI? Why now?
Few sectors test technology as rigorously as aviation security (AVSEC). Every system is scrutinised, every process audited and every decision made under pressure.
That’s why what works here often sets the standard elsewhere. AI is no exception. The lessons emerging from AI’s deployment in aviation – from how to integrate it safely to how to maintain human trust – are directly relevant to all security domains, from critical infrastructure and border protection to event and urban surveillance.
Airports offer the ultimate stress test: Data-rich, high-stakes and relentlessly time-sensitive. If AI can function reliably at the checkpoint, it can function almost anywhere. But achieving reliability means translating tech into practice, ensuring algorithms serve operations – and not the other way around.
From algorithm to airside
There’s no shortage of clever AI tools in circulation, anomaly detection, object recognition, predictive maintenance, generative threat-image training, crowd analytics and more.
The problem is that few make it out of the pilot stage. Moving from lab to live environment is hard: Messy data, interconnected systems and unforgiving human factors.
For AI to earn its place airside, it must meet several non-negotiables: Comply with strict aviation and data-protection regulations; be explainable to humans, not just accurate on paper; fit seamlessly into established workflows; win the trust of screeners and operations staff; strengthen cybersecurity, not weaken it.
The difference between success and shelfware lies in translation: Starting with the outcome, not the algorithm. What operational problem are we solving? How does the tool help organisations perform better, not just differently?
Eyes on the sky, code on the ground
Despite the hurdles, AI is already delivering quiet and measurable value in targeted use cases.
Established remote screening systems, coupled with APIDS (Automated Prohibited Item Detection Systems), will offer significant improvements in throughput once APIDS achieves regulatory approval for what is known as image on alarm.
The AI behind APIDS is a challenge in the tightly regulated world of AVSEC, but in other security environments this use of AI is much easier to adopt.
These threat detection algorithms need data for training, which is often difficult to come by. Here too AI can help with synthetic data generated to provide thousands of bags containing accurate representations of items both innocent and threat.
Elsewhere, tray analytics reduce human error by spotting left-behind items, improper tray loading and suspicious interactions with trays. Predictive maintenance models forecast component failures before they happen, ensuring security equipment is fully functional.
Crowd-flow analysis warns operations teams about bottlenecks before they escalate.
None of these examples try to do everything.
They succeed precisely because they do one thing well, supporting human decision-makers, not replacing them. The common thread is clarity of purpose: Better, faster, safer outcomes.
Smart security, real results
The temptation in AI is always to chase the next breakthrough. But the most effective gains come from incremental intelligence, embedding AI into the systems people already use and trust.
Doing that reliably depends on three fundamentals:
- Data discipline – AI lives or dies by data quality. In aviation, data is often siloed, inconsistent or locked inside proprietary systems. Without clean, structured and labelled datasets, even the best model will misfire. Establishing shared data standards and governance is the foundation of every serious deployment
- Open architecture – interoperability matters more than innovation. Open standards such as DICOS and common interfaces make it possible to integrate AI modules without locking into a single vendor. A flexible architecture means airports can test, scale or replace tools without rebuilding the whole system
- Outcome thinking – AI is not a product to install; it’s a capability to integrate. Every deployment should start with a clear performance metric, reducing false alarms, improving throughput, enhancing situational awareness or automating compliance evidence. Outcome-driven design turns experimentation into measurable value
From code to checkpoint
Many promising AI pilots stumble at the operational threshold because they were designed in data labs, not control rooms. The screener’s environment, high pace, high scrutiny no tolerance for error, exposes every mismatch between theory and practice.
Success depends on understanding how to turn algorithmic output into something operators can act on confidently. It’s about designing systems that speak the language of security, compliance, traceability and performance, rather than forcing technology on the organisation.
Before any AI initiative begins, four questions anchor that translation: Which process problem are we solving? What data is needed? Can that data be accessed responsibly? Which technology works best with operational procedures? When those answers align, AI moves from concept to capability.
Governance as an enabler
Regulation is often viewed as a brake on innovation, but in AVSEC it’s the runway that lets AI take off safely. Without assurance, transparency and accountability, no system will ever be trusted at scale.
Recent frameworks, the EU AI Act, the Cyber Resilience Act, NIS2 and the EASA AI Roadmap, provide the scaffolding for “trustworthy AI”. The real task is translating principles into day-to-day operations.
That means ensuring explainability so every AI decision can be traced and reviewed, testing for robustness under real-world edge cases (not just controlled trials) and defining governance clearly across suppliers, operators and regulators.
When these principles are built in early, deployment accelerates instead of stalling. Governance becomes an enabler, not an anchor.
The human factor
Tech may evolve, but security remains human at its core. Screeners’ intuition, pattern recognition and ethical judgement still decide the outcome. What changes is how humans interact with systems.
Tomorrow’s operators must be AI-literate, understanding what the system is showing, when to trust and challenge it.
That requires training in procedure and perception: How AI works, where bias can creep in and how to manage shared accountability between human and machine.
AI adoption is as much cultural as it is technical. Operators and supervisors need to feel ownership of the tools they use. Early involvement, open feedback loops and visible performance gains build that trust.
When teams see AI cutting false alarms or easing workload, confidence follows.
Security programs that treat AI roll-out as a human-performance initiative, not an IT project, make change that lasts.
Is your operation AI-ready?
Before diving into pilots or vendor demos, security leaders should ask a simpler question: Are we ready? This quick checklist helps frame the answer.
- Do you know what problem you’re solving? “We want to use AI” isn’t a goal. Reducing false alarms or improving training is. Start with clarity
- Is your data usable? Structured, labelled and shareable data are prerequisites. Fragmented or proprietary datasets stop AI before it starts.
- Do you have internal capability, or a trusted partner? Someone must be able to challenge vendor claims and map technology to operational reality
- Are your systems interoperable? Modular, standards-based systems allow experimentation and scaling without disruption
- Do your people trust the system? Engage frontline operators early, explain what the AI does and emphasise that it augments, not replaces, their expertise
Start small, scale sensibly and measure impact continuously. Most organisations can’t check every box at once, but progress starts with awareness and focus.
Beyond the buzz
For all the excitement, the AI market around security is noisy and uneven.
Some products can’t explain how they make decisions, can’t be independently validated or add cybersecurity risk through data leakage or adversarial attacks. Others struggle to align with evolving regulatory expectations.
The appetite to innovate is understandable, but the lesson is clear: Speed without assurance is fragility not agility. AI must be auditable, maintainable and justifiable.
The next generation of deployments will depend as much on policy as on processing power.
Fusion over hype
The next real leap in security will not come from buying smarter algorithms but from fusing what already works: Mature processes, rich data and dependable technology.
AI’s greatest value lies in connecting these elements, not overshadowing them. This shift reframes AI from hype to habit. The conversation moves from “what can AI do?” to “what should we do with AI?” – a more grounded question for any safety-critical environment.
Conclusion: Ground truth
AI’s place in AVSEC is no longer theoretical; it’s practical, provided it’s deployed with discipline. The same principles apply wherever complex environments demand trust, speed and accountability.
At Rheinberry Group, this philosophy is formalised through PDT Fusion, the integration of Process, Data, and Technology into a single, adaptive framework. It’s how AI moves from proof of concept to dependable capability: Aligning operational workflows, data governance and technological design so that each reinforces the other.
The opportunity now lies in translating intelligence into operations, responsibly, explainably and human-centrically. Organisations that succeed will start small, prove value early and embed AI where it enhances human judgement rather than erasing it.
AI will not secure aviation or any other domain on its own. But with the right blend of process discipline, data integrity and human insight – the very principles at the heart of PDT Fusion – it can make those who do far more effective, transforming modern security from reactive protection into proactive resilience.
