ISJ speaks exclusively with Igor Kudryashov, PhD – CEO of Quantum Detection.
How is AI improving accuracy and efficiency in security screening?
AI is transforming security screening from reactive image interpretation to proactive risk analysis.
Traditionally, X-ray inspection has relied heavily on operator experience, which naturally varies from person to person, shift to shift. AI introduces consistency, scalability and measurable performance.
At Quantum Detection, we develop AI models designed specifically for real-world security environments – from cabin baggage to high-energy cargo and vehicle inspection.
Modern deep learning systems are capable of detecting prohibited items, anomalies and density irregularities with high reliability.
Beyond object recognition, AI can classify complex threat compositions and even flag inconsistencies between declared cargo manifests and scanned imagery.
Equally important is efficiency. AI reduces operator cognitive load by automatically prioritising alarms and highlighting regions of interest.
This shortens decision-making time while maintaining – and often improving – detection performance.
In high-throughput environments such as airports, borders and infrastructure facilities, even small efficiency gains translate into significant operational impact.
AI also enables continuous improvement.
Through structured feedback loops and controlled dataset expansion – including the use of synthetic data to model rare or emerging threats – screening systems no longer remain static after installation.
They evolve alongside the threat landscape.
Where do you see the balance between automation and human oversight in screening environments?
Security screening should not become fully autonomous and it does not need to.
The most effective model is human-in-the-loop. AI should function as a force multiplier, not a replacement.
It performs what machines do best: Analysing large volumes of visual data with consistency and speed. Humans perform what they do best: Contextual judgment, situational awareness and final decision-making.
In deployments we support at Quantum Detection, automation handles first-pass analysis, anomaly highlighting and repetitive inspection tasks.
Human operators retain authority for adjudication, secondary inspection and response escalation. This balance strengthens operational trust.
Regulators and end-users are far more comfortable deploying AI when it augments trained professionals rather than attempts to replace them.
Properly implemented, AI reduces fatigue and enhances focus, allowing security personnel to concentrate on higher-value decisions.
How do you balance high performance with cost-effective deployment at scale?
High performance alone is not enough. In operational security environments, scalability and cost control determine whether innovation succeeds. At Quantum Detection, we prioritise architecture.
AI must integrate efficiently with existing X-ray platforms without requiring full system replacement. This approach accelerates adoption and preserves prior infrastructure investments.
Modular development also plays a key role. New detection categories can be added incrementally, allowing customers to expand capability without disruptive upgrades.
Synthetic data pipelines reduce development time and cost while maintaining validation rigor. Security screening is entering a new phase. AI is no longer experimental.
The focus now is responsible deployment – combining performance, oversight and scalability to create systems that are both intelligent and practical.
