Best AI Use Cases for Security Professionals

The cybersecurity landscape quickly and constantly evolves, which means security professionals will always struggle to keep up with the new tactics and technology cybercriminals use.

While there are steps security teams can take to stay a step ahead of threats like phishing and social engineering that are increasing in sophistication with the use of AI, these security professionals must continually stay on top of their cybersecurity strategy and monitor the progress of both their automated AI-based tools and their organization's employees. Threat actors move fast; and security professionals must move even faster.

Best Practices

The key to staying one step ahead of their cyber adversaries is for security professionals to incorporate AI capabilities into their multi-layered defense strategy. That means using security solutions that take advantage of AI's strengths, and then combining those strengths with other security solutions to backstop against its weaknesses. The result should be a broad and layered cyber defense system that combines the latest in machine intelligence with the best of rules-based and other types of security controls, all continually monitored and evaluated by the brains of human security operations center (SOC) analysts.

While AI is adept at recognizing and stopping the impact of common threats at scale, and can do so with greater accuracy than human beings, to stop truly dangerous attacks, organizations need a comprehensive security architecture that deploys AI-powered filtering designed by data science experts who know how to navigate the gray area between clear threats and emails or links that are legitimate and crucial to business operations. Since no solution is perfect, this should also be supported by feedback loops to help quickly identify where machine learning models are not quite hitting the mark.

In practice, this means first deploying AI where a lot of data exists. For example, AI was first used in cybersecurity to identify anomalies in user behavior and network traffic.

Today, the synergy of human expertise and algorithmic prowess is propelling innovation to new heights. Symbiotic 'human-in-the-loop' support is not just a framework, it's the linchpin that delivers unparalleled precision and adaptability. As algorithms navigate data, human insight brings contextual understanding, ethical discernment, and a nuanced touch.

This dynamic collaboration between AI and human crafts a future where their convergence becomes the catalyst for groundbreaking advancements, ensuring a harmony that not only surpasses automated capabilities but also resonates with the essence of our shared human experience to make organizations more secure.

At the same time, it is important for security professionals to remember that AI and machine learning technologies aren't inherently superior. The effectiveness of machine learning relies heavily on the quality of data on which it's trained. Poor data can lead to flawed machine learning outcomes and moreover, the human intelligence guiding its development plays a crucial role. Incorrect decisions during the "learning" phase can result in biased or inaccurate results.

With these best practices in mind, security professionals need to take a look at some of the very practical applications of AI in cybersecurity, how they work, and their benefits.

Malicious URL Detection	URL detection identifies malicious URLs, combining dozens of scanning layers working together to detect high-risk URLs as effectively and efficiently as possible.
Defending Against Business Email Compromise	AI can use algorithms and natural language processing (NLP) to effectively detect targeted email threats, empowering users with information, limiting attackers' information gathering capabilities, and keeping malicious emails from ever being delivered to users.
Stopping Outbound Emails and Sensitive Data from Falling Into the Wrong Hands	Misaddressed Email Protection uses AI to track users' communications, identify anomalies, and alert employees if they are about to send an email to a new or unrecognized address.
Catching Malicious Emails Disguised as Legitimate Messages from Credible Sources	Credential harvesting protection uses machine learning and advanced computer vision to check whether a URL is legitimate, preventing users from inadvertently providing their login credentials to cybercriminals.
Categorizing and Triaging Suspicious emails and Websites	Supervised learning categorizes websites as malicious or inappropriate, blocking access to those sites.
Identifying "Not Safe for Work" Images	Deep learning and computer vision algorithms work to detect inappropriate images in emails, helping to maintain a safe and professional work environment.
QR Code Detection	QR code detection can not only detect QR codes through deep learning and computer vision algorithms, but the link residing behind the QR code is resolved and passed to URL detection to identify high risk URLs.
Malware and Zero-Day Protection	Files are sent to a sandbox and analyzed by advanced machine learning algorithms for decoys, anti-evasion techniques, anti-exploits, and aggressive behavior analysis resulting in efficient malware detection.

The Bottom Line

As AI continues to increase in practical and effective use in cybersecurity tools, security professionals need to continually analyze their environment, their users, their solutions, and the vendors that provide those solutions to find the best balance possible between all four. This can be a real challenge, but is something that is achievable when partnering with the right vendors. Learn more about how to do this with The Promise and Truth of the AI Security Revolution.

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