How to Build Trustworthy and Secure AI for Cyber Resilience
▼ Summary
– Securing AI systems themselves is now a critical cybersecurity priority, as attackers increasingly target the AI supply chain to compromise data and models.
– A robust AI defense strategy requires visibility into system behavior, explainability of AI decisions, and continuous validation through testing and monitoring.
– Key AI-specific threats include data poisoning, model theft, and prompt injection attacks, which necessitate proactive and adaptive defenses.
– Security must be embedded from the start using a “secure-by-design” approach to avoid sacrificing system performance or safety later.
– Building trustworthy AI is a continuous journey that depends on collaboration across teams and anchoring development on principles of resilience.
The integration of artificial intelligence into critical operations demands a fundamental shift in cybersecurity strategy. Securing the AI systems themselves is now as crucial as using AI for defense, moving beyond prevention to ensure these technologies can withstand, recover from, and adapt to sophisticated attacks. This approach, known as cyber resilience, is essential for building trustworthy and robust AI.
Dr. Vrizlynn Thing, Senior Vice President and Head of the Cybersecurity Strategic Technology Centre at ST Engineering, emphasizes that resilience thinking applies directly to artificial intelligence. In complex cyber-physical systems, the goal is to design for endurance and adaptation, not just to block threats. This same mindset must be applied to AI development, where innovation and protection evolve together through rigorous testing and the implementation of adaptive defenses.
A primary reason for this focus is the evolving threat landscape. Adversaries are increasingly targeting the AI supply chain itself. They may poison training data, manipulate models, or exploit vulnerabilities during deployment. A compromised AI system can make skewed decisions silently, posing severe risks, especially for autonomous operations. Therefore, embedding security from the initial design phase through to ongoing operations is non-negotiable.
Several AI-specific attacks currently present significant dangers. Data poisoning remains a top concern, as it corrupts the learning process at its source. Model inversion and theft attacks aim to extract proprietary intellectual property from trained models. Furthermore, the rise of generative AI has introduced prompt injection as a fast-moving, real-world threat. These evolving risks make continuous stress-testing of AI systems against novel attack methods a critical practice.
A practical and robust AI defense strategy rests on three pillars: visibility, explainability, and continuous assurance. You cannot protect what you cannot see; comprehensive visibility into data flows and model behavior is essential for early threat detection. Explainability is equally vital, understanding why an anomaly occurred is what drives true resilience and makes AI systems auditable. Finally, security assurance must be an ongoing process, involving advanced testing, proactive red-teaming, and lifecycle protection measures.
Businesses often struggle to balance AI performance with stringent security, especially when safeguards are added as an afterthought. The solution is to embed secure-by-design principles from the very beginning. This involves implementing lightweight, adaptive defenses that protect without crippling performance. Organizations can build on global frameworks like the NIST AI Risk Management Framework or Singapore’s Model AI Governance Framework, but true resilience requires going beyond mere compliance to bridge policy with technical practice.
As threats grow in sophistication, defenders must move with equal speed. The industry is shifting from reactive defense to proactive resilience. Keeping AI systems safe is a continuous journey that depends on collaboration across security teams, developers, and policymakers. Anchoring development on the foundational principles of resilience and trust is what transforms powerful AI into responsible AI.
In practice, this means helping customers assess the security robustness of their AI, benchmarking against industry standards, and testing across multiple attack scenarios. Providing clear explainability for findings allows organizations to remediate issues and implement appropriate protection mechanisms as their AI evolves.
ST Engineering’s AGIL® SecureAI platform operationalizes these ideas. It proactively identifies and mitigates threats through pre-deployment testing and in-production monitoring, enabling organizations to scale AI innovation securely. Looking forward, AI will continue to be both a powerful enabler and a prime target. The future of secure AI depends on designing resilience in from the start, combining adaptive defenses, explainable models, and ongoing validation to sustain trust in an increasingly digital world.
(Source: InfoSecurity Magazine)
