What are The Three Waves of AI in Cybersecurity?

There is no single governing authority for the use of AI in cybersecurity. However, several other groups are involved in developing guidelines and best practices for the use of AI in cybersecurity. These include the World Economic Forum, the MITRE Corporation, the IEEE, and DARPA.

Who is DARPA, and Why Does Their Opinion Matter?

DARPA is the Defense Advanced Research Projects Agency, a United States Department of Defense research agency. DARPA is responsible for developing new technologies for the military and has a long history of funding innovative research in cybersecurity.

DARPA’s opinion matters because they are one of the leading research agencies in the world that have helped shape the field of cybersecurity and provide valuable insights into where the industry's future is headed.

What are The Three Waves of Cybersecurity?

The field of cybersecurity has evolved significantly over the past few decades. In the early days, cybersecurity focused on preventing unauthorized access to computer systems. However, as cyberattacks have become more sophisticated, cybersecurity has had to evolve to keep pace.

In 2013, DARPA outlined three waves of AI that represent different cybersecurity approaches, each with strengths and weaknesses.

First Wave: Prevention

The first wave of cybersecurity focused on preventing unauthorized access to computer systems. This was done through various methods, including firewalls, intrusion detection systems, and access control lists. The first wave of cybersecurity aimed to create a "hardened" perimeter around computer systems that would make it difficult for attackers to gain access.

Legacy Rules-Based Approach:

This process typically involves creating rules based on known patterns and indicators of

cyber threats. These rules can be designed to detect specific types of attacks, such as

malware signatures, known vulnerabilities, or suspicious network behavior. The model is

then utilized to process and analyze security logs, network traffic, and other relevant data,

helping to identify potential threats based on the predefined rules. While this approach can effectively detect known threats, it has limitations when identifying new or evolving threats.

Strengths:

• The first wave of cybersecurity was effective at preventing simple attacks.
• It was relatively easy to implement and manage.

Weaknesses:

• The first wave of cybersecurity was not effective against sophisticated attacks.
• It could not protect against attacks that exploited vulnerabilities within the system.

Second Wave: Detection and Response

The second wave of cybersecurity focused on detecting and responding to cyberattacks. This was done through various methods, including intrusion detection systems, honeypots, and incident response teams. The second wave of cybersecurity aimed to identify and contain cyberattacks as quickly as possible.

Machine-Learning Models:

ML models are trained on large datasets that include normal and malicious behavior,

enabling them to learn patterns and identify anomalies that may indicate potential threats.

ML models can be applied to various use cases, including threat detection, malware

analysis, user behavior analytics, and vulnerability management.

Strengths:

• The second wave of cybersecurity was more effective at detecting and responding to sophisticated attacks.
• It could protect against attacks that exploited vulnerabilities within the system.

Weaknesses:

• The second wave of cybersecurity was reactive, meaning that it could only respond to attacks that had already occurred.
• It was still not effective against attacks that were designed to evade detection.

Third Wave: Real-time Detection, Prediction and Prevention

The third wave of cybersecurity is still in its early stages but is focused on predicting and preventing cyberattacks. This is done through various methods, including machine learning, artificial intelligence, and behavioral analytics. The third wave of cybersecurity aims to identify and mitigate risks before they become attacks.

Strengths:

• The third wave of cybersecurity has the potential to be more effective than previous waves at preventing cyberattacks.
• It can be proactive, meaning it can take steps to prevent attacks before they occur.

Weaknesses:

• The third wave of cybersecurity is still in its early stages, and it has yet to be determined how effective it will be.
• It can be expensive to implement and manage.

Where does Generative AI fit in?

Generative AI is a type of AI that can create new data, such as text, images, and code. This makes it a powerful tool for cybersecurity, as it can generate realistic phishing emails, malware samples, and other attack vectors. Generative AI can also create new defensive techniques like honeypots and deception campaigns.

DARPA believes that generative AI has the potential to revolutionize cybersecurity and make it much more difficult for attackers to succeed. The agency is funding research into generative AI for cybersecurity and is also developing its own generative AI capabilities.

Here are some specific examples of how generative AI is being used in cybersecurity today:

• Generating phishing emails: Generative AI can create realistic phishing emails that are more likely to trick users into clicking on malicious links.
• Generating malware samples: Generative AI can create new malware samples that are more difficult to detect and analyze. 
• Creating honeypots: Generative AI can be used to create honeypots that are more attractive to attackers.
• Developing deception campaigns: Generative AI can be used to develop more effective deception campaigns to mislead attackers. 

These are just a few examples of how generative AI is used in cybersecurity today. As generative AI technology develops, it will likely play an even more critical role in protecting organizations from cyberattacks.

MixMode: The Only Generative AI for Real-Time Threat Detection and Response

MixMode has developed the world's first commercially available intuitive threat detection and response platform built on Third Wave AI.

MixMode’s generative AI is uniquely born out of dynamical systems (a branch of applied mathematics) and self-learns in an environment without rules or training data. MixMode’s AI constantly adapts itself to the specific dynamics of an individual network rather than using the rigid legacy ML models typically found in other cybersecurity solutions.

The MixMode Platform is the only generative AI cybersecurity solution built on patented technology purpose-built to detect and respond to threats in real-time, at scale. The MixMode Platform autonomously ingests and analyzes data at scale to cut through the noise, surface critical threats, and improve overall defense against attacks. 

Choosing the Best Approach

The three waves of cybersecurity represent different approaches to protecting organizations from attacks. Each wave has strengths and weaknesses; the best strategy for a particular organization will depend on its specific needs.

As cyberattacks continue to evolve, organizations need to stay up-to-date on the latest trends in cybersecurity. Organizations can make informed decisions about protecting their infrastructure from attack by understanding AI and utilizing its benefits to ensure protection against today’s sophisticated threats.