Techie Tonic: Who is ready for LLM security? A CEO's perspective to AI resilience

From a CEO's perspective, while resilience is widely recognized as a top strategic priority, many leaders believe their organisations are not yet fully prepared for future disruptions. The direction of movement is towards embedding resilience into the core strategy and culture, but implementation is often a work in progress.

As large language models (LLMs) become deeply integrated into business operations, public services, and consumer applications, the question of who is ready for LLM security has moved to the center of modern cybersecurity discussions. While no single group can claim to be fully prepared, because the threat landscape evolves faster than established standards, several categories of organizations have begun building meaningful readiness. Their approaches shed light on what “being ready” actually means in the era of generative AI.

The red-team approach

Security-focused technology teams, including those in advanced research settings and internal security divisions, represent one of the most prepared groups when it comes to LLM security. They evaluate models continuously, treating them like any other potentially vulnerable system that must be rigorously tested, monitored, and even intentionally attacked to strengthen its defenses. Their work often involves generating adversarial prompts to uncover weaknesses, auditing how models behave in edge cases or ambiguous scenarios, and assigning risk scores to threats such as prompt injection, data leakage, or unsafe outputs. By building their own specialized tools for red-teaming and verification, these teams can rapidly detect emerging exploitation techniques. As per Many CXOs community members: even though industry-wide standards are still evolving, the internal frameworks created by the community are becoming early signs of genuine security maturity in the LLM ecosystem.

Enterprises deploying LLMs at scale, whether for customer support, analytics, automation, or internal tools are building operational readiness by focusing on how these models function within real-world workflows rather than on the underlying model itself. Their preparedness is reflected in shared practices such as enforcing strict input and output filtering, limiting access to sensitive or high-impact queries, monitoring responses for anomalies, isolating models within sandboxed environments, and establishing clear escalation protocols for unsafe or unexpected outputs. This level of readiness emerges not from a single tool but from coordinated efforts across security teams, legal groups, and AI developers. In these organizations, the model is treated as one component of a larger system, and security is designed to wrap around the entire ecosystem instead of relying solely on the model’s built-in safeguards (Guardrails).

Governing the future: Policy, standards and training

Communities dedicated to policy, standards, and training form another important pillar of LLM security readiness, focusing on education and governance rather than tool building. Such Communities work to equip security professionals with the knowledge needed to navigate emerging risks through courses and workshops on topics such as prompt manipulation, data privacy challenges, model-specific vulnerabilities, and defensive engineering. They also develop taxonomies that categorize threats like jailbreaking, role hijacking, token smuggling, and hallucination exploitation, alongside evaluation frameworks that help organizations convert technical risks into clear policy requirements. This expanding body of knowledge is vital for raising readiness across industries, particularly for organizations without specialized AI security teams. By clarifying how LLM-related threats differ from traditional software issues, these communities lay the groundwork for safer and more responsible deployments.

Also observed, AI research teams focused on guardrails and governance form a fourth group demonstrating strong readiness in LLM security, developing formal safety systems and protective layers that operate independently of the underlying model. Their work centers on creating policy-driven controls that can sit before or after an LLM, regulating both the inputs it receives and the outputs it generates. Common approaches include building policy enforcement engines that define unacceptable behaviours, designing output validation models that screen for safety or compliance issues, developing multimodal guardrails that work across text, images, code, or audio, and implementing rapid update mechanisms to address emerging threats quickly. These efforts underscore a key principle “securing LLMs cannot rely on the model alone but requires layered defenses that surround and support it”.

Conclusion

Realised, no one is fully ready for LLM security, yet some groups are clearly moving in that direction. Readiness today means continuous testing, layered defenses, organizational alignment, and adaptive governance. As threats evolve, the organizations that succeed will be those that treat LLMs not as static tools but as dynamic systems requiring constant oversight and improvement.