Artificial intelligence in the World of Cyber Protection: What the Future Holds
Artificial intelligence (AI) has been a prerequisite in cybersecurity for several years now, but the widespread adoption of Large Language Models (LLMs) made 2023 a notably thrilling year. In fact, LLMs have already started revolutionizing the entire landscape of cybersecurity. However, it is also generating unparalleled challenges.
On one hand, LLMs make it simple to process large amounts of information and for everyone to exploit AI. They can deliver tremendous efficiency, intelligence, and scalability for handling vulnerabilities, preventing attacks, managing alerts, and responding to incidents.
On the flip side, adversaries can also harness LLMs to make attacks more efficient, exploit additional vulnerabilities introduced by LLMs, and misuse of LLMs can create more cybersecurity problems like unintentional data leakage due to the widespread use of AI.
Deployment of LLMs demands a new approach to thinking about cybersecurity. It is much more dynamic, interactive, and customized. In the past days of hardware products, hardware was only changed when it was substituted by the next new edition of hardware. In the era of cloud, software could be updated and customer data were collected and analyzed to enhance the next edition of software, but only when a new edition or patch was released.
Now, in the new era of AI, the model used by customers has its own intelligence, can keep learning, and change based on customer usage — to either serve customers better or skew in the wrong direction. Therefore, we not only need to build security by design – make sure we build secure models and prevent training data from being poisoned — but also continue evaluating and monitoring LLM systems after deployment for their safety, security, and ethics.
Most importantly, we must integrate built-in intelligence in our security systems (like instilling the right moral standards in children instead of just regulating their behaviors) so that they can be adaptive to make the right and robust judgment calls without easily getting influenced by bad inputs.
What have LLMs brought for cybersecurity, good or bad? I will share what we have discovered in the past year and my forecasts for 2024.
When it comes to the growing use of AI in cybersecurity, it is evident that we are at the beginning of a new era – the early stage of what’s often referred to as “hockey stick” growth. The more we comprehend about LLMs that enable us to enhance our security posture, the better the likelihood we will be ahead of the curve (and our adversaries) in getting the most out of AI.
While I believe there are a multiplicity of areas in cybersecurity poised for discussion about the growing use of AI as a force multiplier to combat complexity and widening attack vectors, three things stand out:
AI models will make huge steps forward in the creation of in-depth domain knowledge that is rooted in cybersecurity’s wants.
2. Use cases
Transformative use cases for LLMs in cybersecurity will emerge. This will make LLMs indispensable for cybersecurity.
3. AI security and safety
In addition to using AI for cybersecurity, how to build secure AI and secure AI usage, without jeopardizing AI models’ intelligence, are major topics.