As we have written about in other blog posts, convolutional neural networks (CNN) have revolutionized computer artificial intelligence (AI). AI has allowed computers to both process and create data in completely new ways. This presents challenges and opportunities in the cyber security space. This post will look at how cyber defense systems are using AI to enhance computer security.
Other types of AI, such as Bayesian filters for electronic mail systems have long been in use. Now both attackers and defenders have much more powerful tools, particularly using Large Language Models (LLM) that can generate text more humanly. Specific models can be trained for security systems to detect attacks earlier and cover a larger attack surface than just one tool like an e-mail filter.
Natural language processing (NLP) which predates LLMs, and is more algorithmic, rather than neural network based can be used to detect and filter out attacks. This is done by analyzing textual relationships and tagging parts of the text. NLPs can provide a score on incoming data of the likelihood that data is part of an attack, and then security systems can process the data based on that score.
AI powered behavioral analysis is a way to train an AI model on how bad actors conduct cyber-attacks. When systems are under attack, the AI models can detect what is happening faster, so defenses against the attack can be used sooner and can stop it before any damage is done.
Network security solutions use the tools detailed above to manage communications between computers. If the communications are safe, they can pass, otherwise they are blocked, filtered, and dropped. This can sometimes prevent an attack, but even when an attack is successful, it will often dramatically reduce the impact. Rather than many computers infected with a virus, for example, only one computer is, and then it is automatically isolated from the other computers.
A challenge with AI is that it is a powerful tool that both the attackers and defenders have equal access to. As attacks become “amplified” with AI, it is likely that they will also need to be defended by AI. Fortunately, while CNNs are relatively new other fields of AI have been involved in cyber security for a long while and are well known in both effectiveness and implementation. Adding new CNN based models to traditional systems to enhance their ability to detect and mitigate the new threats is mandatory.
For non-cybersecurity focused organizations, this means looking at your existing cyber defenses, and continuously evaluating if they still meet the needs. Some examples are firewalls that may be getting old are likely only supporting basic IP address-based filtering policies, rather than rich rules that are content based. Also, authentication systems need to perform more extensive checks to mitigate token theft.
If you are unsure of the security systems protecting your business, or want to chat more about how AI can keep your business safe Contact us!