AI is transforming safety-critical systems across defence and high-stakes industries. Recent research from the Alan Turing Institute highlights that AI assurance – validating, documenting, and mitigating AI risk – is now essential for safe deployment. With AI systems growing in complexity and autonomy, traditional safety approaches alone are no longer sufficient. *
Clients frequently face three main challenges:
- Complexity of AI risks: understanding potential failure modes, unintended or unwanted behaviour, implementing mitigations, and keeping pace with policy and regulation.
- Resource pressures: high-quality AI safety work is intensive, and finding engineers with the right combination of technical skill and practical experience is challenging.
- Lack of standardised assurance frameworks: Turing research confirms there is no universally adopted approach, making expert guidance essential.
Our Principal Safety Consultant, Scott Longmuir, brings the latest expertise in safety-critical AI systems, having recently completed his MSc in Safety-Critical Systems Engineering (view post). His research explored how integrity and assurance processes can be applied to machine-learning-enabled systems.
Scott is part of a highly experienced team of engineers and safety specialists, providing practical support across multiple domains and platforms. With a collaborative, human-centred approach, the team can help ensure that clients’ AI systems are assessed, mitigated, and managed safely – even in the most complex environments.
“Managing AI in safety-critical systems isn’t just about technology. It’s about understanding the risks, applying robust assurance practices, and giving clients confidence that their programmes are safe and reliable. That’s what we do at SQEP every day.” Scott Longmuir, Principal Safety Consultant, SQEP
💡We’d love to know… what’s the biggest challenge you face in managing AI risks in safety-critical systems?
🔗 Read The Alan Turing Institute Report in full.
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