Understanding AI Security: Definition and Explanation
On a basic level, artificial intelligence (AI) security solutions are programmed to identify “safe” versus “malicious” behaviors by cross-comparing the behaviors of users across an environment to those in a similar environment. This process is often referred to as “unsupervised learning” where the system creates patterns without human supervision. For some AI platforms, like Vectra, “deep learning” is another key application for identifying malicious behaviors. Inspired by the biological structure and function of neurons in the brain, deep learning relies on large, interconnected networks of artificial neurons. These neurons are organized into layers, with individual neurons connected to one another by a set of weights that adapt in response to newly arriving inputs.
Sophisticated AI cybersecurity tools have the capability to compute and analyze large sets of data allowing them to develop activity patterns that indicate potential malicious behavior. In this sense, AI emulates the threat-detection aptitude of its human counterparts. In cybersecurity, AI can also be used for automation, triaging, aggregating alerts, sorting through alerts, automating responses, and more. AI is often used to augment the first level of analyst work.
Common Applications of AI in Cybersecurity
AI security solutions have a wide range of applications in the realm of cybersecurity. Here are some of the most common uses:
- Threat Detection and Prediction: AI can analyze large datasets to identify activity patterns indicative of potential malicious behavior. By learning from previously detected behaviors, AI systems can autonomously predict and detect emerging threats.
- Behavior Contextualization and Conclusion: AI can contextualize and draw conclusions from incomplete or new information, aiding in the identification and understanding of cybersecurity events.
- Remediation Strategy Development: AI tools can suggest viable remediation strategies to mitigate threats or address security vulnerabilities based on their analysis of detected behaviors.
- Automation and Augmentation: AI can automate various cybersecurity tasks, including alert aggregation, sorting, and response. It complements the work of human analysts, enabling them to focus on more complex challenges.
Benefits of Leveraging AI Technologies in Security
The adoption of AI cybersecurity solutions offers several advantages for organizations and their IT and security teams:
- Enhanced Data Processing: AI's capabilities enable the processing of large volumes of data at high speed, providing organizations with comprehensive insights into potential threats.
- Augmentation for Resource-Constrained Teams: AI fills the resource gap for smaller or less resourced cybersecurity teams by automating routine tasks and providing continuous protection.
- Consistent and Long-Term Protection: AI systems provide consistent and continuous protection, reducing the risk of human error and offering long-term defense against evolving threats.
Evaluating an AI Cybersecurity Vendor for Your Network
Choosing the right AI security vendor is crucial for ensuring the effectiveness and compatibility of the solution with your network. Here are some key questions to consider when evaluating AI cybersecurity vendors:
- Machine Learning Algorithms: What types of machine learning algorithms does the vendor's product utilize? How frequently are these algorithms updated and new ones released?
- Algorithm Performance: How quickly can the machine learning algorithms detect threats in a new environment? Do they require a learning period, and if so, how long does it take?
- Prioritization and Integration: How does the product prioritize critical and high-risk hosts that require immediate attention? Does it seamlessly integrate with existing detection, alerting, and incident response workflows?
- Integration with Security Infrastructure: Does the product integrate with firewall, endpoint security, or network access control (NAC) systems to block or contain detected attacks? How well does it integrate with these platforms?
- Workload Reduction and Efficiency: What is the expected workload reduction for security analysts using the product? How much efficiency improvement can be anticipated?
- Real-World Testing: Does the product support running red team exercises to demonstrate the value of machine learning algorithms in real-world scenarios? Is the vendor willing to cover the costs if the product fails to detect any threats?
- Product Evaluation: Do they recommend providing remote access to human analysts during the evaluation? What are the reasons behind this recommendation?
Vectra: Empowering Security Teams with AI
Vectra AI, a leading provider of Network Detection and Response (NDR) solutions, leverages AI technology to deliver maximum security for your systems, data, and infrastructure. By detecting and alerting your security operations center (SOC) team of suspicious activities, both on-premises and in the cloud, Vectra AI enables swift and precise action against potential threats. With AI-driven genuine threat identification, your team can focus on critical tasks, free from false alarms.
In conclusion, AI security offers organizations a powerful means of fortifying their cybersecurity defenses. By leveraging advanced machine learning algorithms and deep learning techniques, AI can enhance threat detection, automate processes, and provide continuous protection. When selecting an AI cybersecurity vendor, careful evaluation and consideration of their algorithms, integration capabilities, and real-world performance are essential. With the right AI solution in place, organizations can effectively combat the evolving landscape of cyber threats and secure their digital assets.