Unleash the power of true AI: MixMode's cutting-edge cybersecurity solution

Joe Aliganello Vice President of Product Marketing

Joe is MixMode's Vice President of Product Marketing. He has led product marketing for multiple cybersecurity companies, including Anomali, FireEye, Neustar, Nextel, and various startups. Originally from New York, Joe lives in the DC suburbs and received his bachelor's degree from Iona College.

In the ever-evolving world of cybersecurity, the role of artificial intelligence (AI) is becoming increasingly important. As attackers continue to evolve, defenders need more powerful tools than ever. Artificial intelligence (AI) has emerged as a game changer in this battle, offering both incredible opportunities and unforeseen challenges.

Pros: AI defense

Ultra-fast threat detection: Traditional methods can be difficult to keep up with the ever-increasing number of cyber threats. However, AI can analyze vast amounts of data in real time and identify suspicious patterns and anomalies that could indicate an attack. This allows security teams to respond quickly and minimize potential damage.
Automate routine things: Security professionals are often bogged down with repetitive tasks like log analysis and incident response. AI can automate these processes, saving valuable time for strategic planning and proactive defense.
Predict the unpredictable: AI can analyze past cyberattacks and identify trends, allowing security teams to predict future threats. This proactive approach is critical to staying ahead of the curve in an ever-evolving cyber environment.

The bad: The dark side of AI

Evolve with your enemies: As the AI improves its defenses, attackers quickly adapt as well. AI can be leveraged to develop more sophisticated attacks that bypass traditional security measures and exploit vulnerabilities in the AI-powered systems themselves.
Bias issues: An AI algorithm is only as good as the data used to train it. Any bias in this data can create blind spots in AI security systems and allow certain vulnerabilities to remain undetected. Additionally, biased algorithms can lead to legitimate users being treated unfairly.
“Skynet” scenario: While still a dystopian nightmare, the prospect of attackers gaining control of AI-powered security systems is frightening. Such a scenario can have devastating consequences.

Beyond machine learning with predefined rules

Artificial intelligence (AI) is emerging as a transformative force in cybersecurity, but there are critical differences in the realm of AI-powered solutions. This difference lies in the underlying methodology employed and has a significant impact on effectiveness in an ever-evolving cyber threat environment.

Limitations of rule-based machine learning

Many of the currently available AI solutions for cybersecurity rely heavily on machine learning (ML) algorithms that are trained based on predefined rules. Although these solutions are effective in identifying established threats, they also have significant limitations, including:

Limited learning ability: These systems are adept at recognizing patterns in the data they are trained on, essentially acting as advanced pattern matching tools. But they are struggling to adapt to entirely new attack vectors, leaving them vulnerable to zero-day exploits and other unexpected threats.
False positives and false negatives: The strict nature of predefined rules can lead to high false positive rates and inundate security teams with irrelevant alerts. Conversely, these systems may miss entirely new threats that do not conform to established patterns.
Reactive approach: These solutions excel at responding to past threats based on established patterns. However, they lack the ability to proactively identify and stop emerging threats, which is critical in a dynamic cybersecurity environment.

DARPA’s three waves of AI

The Defense Advanced Research Projects Agency (DARPA), the U.S. Department of Defense's renowned research agency, has defined three different waves of AI that represent different approaches to cybersecurity.

The first wave of AI includes automated systems driven by human-written rules, often leading to high operational costs, false positives/negatives, and failure to detect zero-day threats. The second wave includes statistical methods such as neural networks and machine learning, but these require large amounts of labeled data and struggle to detect new attacks.

We are now reaching the third wave of AI: contextual inference. This innovative approach, pioneered by MixMode in the cybersecurity space, leverages self-supervised, explainable AI to learn and adapt on its own, independent of rules or training data. By understanding the context of your environment, MixMode's AI can not only detect known threats, but also identify the most elusive anomalies that could indicate a potential attack.

Unlike traditional solutions, MixMode's AI is self-sufficient and requires no rules, adjustments, or maintenance. It constantly learns and adapts to the unique dynamics of each customer's network, enabling it to detect both known and unknown threats in real-time. This self-supervised learning capability allows MixMode to provide unparalleled protection against the ever-evolving cyber attack landscape, including zero-day exploits and supply chain attacks.

Download our ebook, Self-Reliant AI-Driven Cybersecurity, to learn more about the differences and explore the power of MixMode's 3rd wave AI approach.

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