Close Menu
  • Home
  • Starting a Business
  • Managing a Business
  • Growing a Business
  • Events
  • Join Now
Facebook X (Twitter) Instagram
Facebook X (Twitter) Instagram YouTube LinkedIn
The Small Business Site
Subscribe
  • Home
  • Starting a Business
  • Managing a Business
  • Growing a Business
  • Events
  • Join Now
The Small Business Site
  • Home
  • Starting
  • Managing
  • Growing
  • Events
  • Join Now
AI

Best AI use cases for Security Professionals

Article by: Mimecast

Awareness of Best Practices and Emerging Technology Can Make All the Difference

Key Points

The key to staying one step ahead of cyber adversaries is for security professionals to incorporate AI capabilities into their multi-layered defense strategy.

Security professionals need to spend the extra time needed to gain a thorough understanding of how these AI capabilities work, and they benefit their security team and their organization as a whole.

They also need to continually evaluate their environment and the AI cybersecurity solutions they deploy because the threat landscape continually changes and evolves.

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 behaviour 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 analysed by advanced machine learning algorithms for  decoys, anti-evasion techniques, anti-exploits, and aggressive behaviour 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 analyse 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.

Mimecast is a proud Partner of the NSBC.

Share this:

  • Email
  • Facebook
  • Twitter
  • Pinterest
  • LinkedIn
Share. Facebook Twitter LinkedIn WhatsApp Email
Previous ArticleThe importance of time and attendance
Next Article Sourcefin lands R150m investment from Futuregrowth to boost SME lending
NSBC Editorial Team
  • Website

Related Posts

Cyber Security

The Journey to Cyber Resilience

Article by: Securicom

Understanding human risk: The 48% factor

April 24, 2025
Cyber Security

Securicom’s e-Purifier: fortifying your email security in today’s threat landscape

Article by: Securicom
Cyber Security

Safeguarding your business – 5 essential data protection tips every small business needs

Article by: Mastercard
Categories
Sponsored Ad:
Facebook X (Twitter) Instagram YouTube LinkedIn
© 2025 National Small Business Chamber (NSBC).

Type above and press Enter to search. Press Esc to cancel.