Agentic AI Will Shape the Future of Managed Security Services?

In the rapidly evolving landscape of cybersecurity, traditional managed security operation systems are increasingly seen as static and rigid, struggling to keep pace with the dynamic nature of cyber threats. The static nature of current software systems, often resulting in costly and time-consuming updates, limits their ability to adapt to the ever-changing cybersecurity landscape. However, the advent of Generative and Agentic AI is poised to revolutionize this space by enabling the creation of "Living Managed Security Operation Systems" (Living MSOS) - adaptive, context-aware solutions that can evolve in real-time, providing a more resilient and responsive approach to protecting digital assets. This article explores the concept of Living MSOS, the pathways to implementing them, and the opportunities and challenges they present in revolutionizing cybersecurity operations.

The Concept of Living Managed Security Operation Systems

Living MSOS represents a paradigm shift from static, inflexible systems to adaptive, context-aware solutions that evolve in real-time to meet the demands of cybersecurity operations. Traditional security systems are built on rigid architectures that require manual updates and interventions to adapt to new threats. In contrast, Living MSOS, powered by Generative AI, operate as dynamic entities that continuously translate human goals into computational actions, adjusting to the nuances of cybersecurity challenges as they arise.

Generative AI, particularly large language models, act as universal translators between human intent and machine operations. This capability allows for more flexible and intuitive interactions between security analysts and security operation systems. For example, rather than manually configuring security settings or updating rules, a security analyst could simply instruct the AI with a natural language command like, "Update the firewall rules to block any traffic from the suspicious IP addresses identified in yesterday's TI report." The AI would then handle the task, interpreting the command and implementing the necessary changes across the system.

Pathways to Implementation

There are two primary pathways to building Living MSOS:

1. Accelerating Traditional Development with Generative AI: This approach involves using Generative AI to enhance the traditional development process, making it faster and more efficient. Generative AI can automate much of the code-writing process, allowing security systems to be updated and adapted more quickly. This pathway is already seeing significant adoption, with estimates suggesting that Generative AI-written code now constitutes a substantial portion of all new code being checked into repositories like GitHub.

2. Creating Adaptive Systems with Agentic AI: The more revolutionary pathway involves using Agentic AI to create truly living systems. These systems would not just automate code generation but also dynamically translate user goals into actions that the system can take autonomously. For instance, an Agentic AI-powered security system could detect a new threat, assess its potential impact, and deploy countermeasures without requiring human intervention.

Opportunities and Limitations

The opportunities presented by Living MSOS are immense. These systems promise to make cybersecurity operations more efficient, responsive, and resilient. By automating routine tasks and enabling more intuitive interactions, Living MSOS could free up security analysts to focus on more strategic activities, such as threat hunting and incident response. Additionally, the adaptive nature of these systems means they can continuously evolve in response to new threats, reducing the need for costly and time-consuming updates.

However, there are also significant limitations to consider. One of the biggest challenges is ensuring that Generative and Agentic AI systems do not introduce new vulnerabilities into the cybersecurity landscape. These systems rely on complex models that are not always transparent, making it difficult to verify their decisions and actions. Moreover, the dynamic nature of Living MSOS could make it harder to maintain consistent security policies and controls, particularly in environments where regulatory compliance is a concern.

Another limitation is the need for new skills and training. Security analysts and other users will need to learn how to effectively communicate with Generative and Agentic AI systems, a skill set that is still in its infancy. While this presents a learning curve, it also offers an opportunity for the cybersecurity workforce to upskill and become more proficient in the use of advanced AI tools.

The Future of MSSPs

As we look to the future, it is clear that Living MSOS will play a critical role in the evolution of MSSPs. These systems, with their ability to adapt and respond to change, represent a significant step forward from the static, rigid systems of the past. However, their success will depend on our ability to harness the power of Generative and Agentic AI while mitigating the risks and challenges they introduce.

In conclusion, while the journey to fully realizing the potential of Living Managed Security Operation Systems is still in its early stages, the opportunities they present for transforming cybersecurity operations are too significant to ignore. By embracing this new paradigm, we can build security systems that not only keep pace with the evolving threat landscape but also anticipate and respond to future challenges in ways that were previously unimaginable.