The new cyber battleground

The last 18 months have seen a prolific increase in activity around cyber analytics, automation, and AI security services in the Middle East region, and indeed globally. Many organizations are struggling to keep up with the pace at which this domain is moving, amid uncertainty about trust, time to value, return on investment, workforce upskilling, and a potential global AI bubble waiting to burst.

Do you have the skills and knowledge to identify critical use cases and implement it with a Trusted AI governance framework across your business? Do you really need AI – or can you solve your challenges with analytics and automation to prove value? These questions are common and necessary to solve “problem zero”: where and how does one start?

In addition to this, cyber adversaries are rapidly advancing their use of AI tools and development techniques to accelerate widescale attacks. In this publication we explore how organizations can realize value and return quickly around AI security.

Current challenges

reduce_capacity

Today, organizations are facing challenges with trust and security of AI, as well as deriving value from the AI investment.

security

As we saw recently, Anthropic was used to launch a fully unmanned cyber-attack across multiple sectors and organizations. This introduces a new threat paradigm and threat actors, mandating organizations to quickly understand and combat the very same AI models and solutions we are using on a day-to-day basis

search

This raises several fundamental questions about the trustworthiness and security of Large Language Models and solutions freely available in the market.

assessment

These solutions are already being integrated into organizations, and have rapidly become trusted solutions, without full consideration of the risks involved.

This raises a wider question around the trustworthiness of AI security, and how organizations can ensure that use cases and AI solutions being implemented do not compromise the risk posture of your organization. Many organizations are considering the establishment of Trusted AI frameworks and processes; however, early adoption is slow. This is a critical first step to reducing the risk of internal AI compromise, but this work impedes the race to create and implement AI security use cases.

A practical roadmapfor trusted AIadoption

Understanding how to safely and securely use AI to defend your organization in the future is nonnegotiable. Adversaries are active in this space, and attacks will again become faster and more sophisticated with the use of AI agents and tools. If you are starting out, identify easier problems to solve while keeping things simple, in addition to acquiring the skills and knowledge to be successful and realize quick time to value and return on investment.

Establish trusted AI security governance
Upskill employees to both secure and use AI for security
Understand the problem you want to solve, then choose the right option (Analytics, Automation, AI, Agentic AI etc.) to show value
Use the tools you have today to create iterative value, then scale (e.g. Microsoft Co-pilot Studio)
Focus on addressing easier challenges first to realize quick time to value and return on investment