Modern Tools for Today’s Growing Threat Landscape

Stop Using 20th Century Technology for 21st Century Security

Warmer days bring greener landscapes. But with this growth comes maintenance, such as weekly grass trimming. The lawn mower has evolved from sheep, to scythe, to reel cutter, to engine-powered blades—now in the computer age, it uses automation to mow consistently and constantly. Trading my gas-powered lawn mower for a robotic one, I finally decided to stop applying 20th century technology to my 21st century landscape. So why are we not doing the same for cybersecurity?  

Similar to my former lawn care approach, many information security solutions—such as antivirus software, firewalls and passwords—are based on 20th century methods. Just because they can do the job, doesn’t mean they do it well in today’s environment. With advances in artificial intelligence (AI), machine learning, processing and memory, legacy security solutions are no longer a match for today’s growing threat landscape. 

For example, consider the challenge of endpoint protection. The majority of my clients have a 99 percent adoption rate of at least one antivirus solution installed on their endpoints. Antivirus solutions have been around for roughly 30 years. So much has changed in that time, and yet companies are still relying on the same technologies for current problems like fileless malware protection. It’s time to upgrade. 

We need smarter, faster solutions to help protect our endpoints in 2018 and beyond. Legacy security falls short because: 

• Anomaly-, behavior- and heuristics-based offerings detect malware post-execution, and yield high false-positive/negative rates; 
• Most are dependent on internet connections to detect threats via cloud signature libraries or cloud behavior analysis; and 
• Micro-virtualization and containerization offerings cause poor endpoint performance, usability challenges and operations failures. 

New solutions coded in recent years are different. For example, products from software company Cylance: 

• Use AI and analyze the core DNA of files to find malware; 
• Detect memory-based exploits to prevent privilege escalations in addition to system attacks; 
• Eliminate the need for signature updates, internet connections or callbacks to a central system for actions on potential malware with machine learning and AI; and 
• Consist of a lightweight agent consuming less than 1-3 percent of CPU, and easy deployment that requires less server, endpoint and administration resources. 

All of this sounds like my new robotic lawn mower: a modern, lightweight solution made possible through new technology such as AI, machine learning and more efficient code. For those still pushing the metaphorical antivirus lawn mower, contact Unisys for a free malware assessment and learn about new tools to maintain a strong security posture in today’s growing threat landscape.

Tags-   Antivirus artificial intelligence (AI) Cylance endpoint protection malware detection

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