In deep instinct, deep learning by Yuda Saydun on Jul 15, 2019

Why a Prevention-First Mindset Has Made a Comeback in Cybersecurity

Advanced persistent threats (ATP) are a significant cybersecurity concern for the modern-day enterprise. Once inside the perimeter, ATPs either expand quickly, causing the loss of data, interruptions to operations, and more, or stay dormant for long periods of time.

These threats require an answer, one that detection-based tools are failing to provide. New solutions, however, are making it possible to prevent cyber threats in real-time, before they can execute or access and hide in wait on a company’s network.

Sealing the Perimeter by Focusing on Prevention

Emerging technologies have finally outstripped the capabilities of detection-based tools, and prevention is now making a comeback in cybersecurity. The most significant advance is the advent of deep learning, a form of artificial intelligence that is being deployed to successfully detect never-before seen malware, zero-day, ransomware, and APT attacks.

Deep Instinct, a CyVent partner, is leading the charge, making it possible pre-empt attacks before they execute rather than trying to hunt, identify, analyze, recover and remediate. As a result, prevention has immediate payback and ROI, eliminating post-breach wheel spinning and false alerts that are holding security teams back.

Already, Deep Instinct’s solutions has proven capable of stopping known and unknown threats in “zero-time.” In a Threat Prevention Evaluation Report from SE Labs, Deep Instinct achieved an industry-first 100% prevention rate and zero false-positives.

Cyber Security Prevention: Why Detection Alone Is Not Enough

Detection-based tools, on their own, simply can’t provide the level of security needed to keep an organization secure. On average, most threats go undetected for upward of 100 days. Because detection-based tools rely on signatures, threats that have yet to be seen readily slip through traditional defenses. A staggering 360,000 new malicious files are detected every day. Breaches will remain a daily occurrence until cybersecurity tools are able to block new threats as quickly as they evolve.

Security professionals also realize that, besides the fear of a major security incident, predicting threats based on machine learning, heuristics, or file reputation provide less-than-perfect accuracy. Security teams are facing a huge volume of false alerts, more than they can realistically manage. The cost of chasing alerts, the reality of overstretched security teams, and the cybersecurity talent gap are all factors causing security professionals to rethink the balance between detection and prevention.

Preparing for the Next Era of Cyber Threats

Threats that lurk on networks for sometimes months at a time are a grave danger that need addressing. Recent tales of the TRITON malware framework show just how deadly ATPs can be, and even the United States power grid is not immune. The question for security teams is how to prevent threats from ever entering a network in the first place. Many are starting to look to a prevention-first strategy that can enhance security for the current threat landscape. With the era of AI versus AI in cybersecurity fast approaching, it’s security tools like Deep Instinct’s that are the way of the future. 

Prevention, however, does not replace detection and response. As with most areas, balance is necessary. Organizations can supplement existing defenses with deep learning technology to prevent attacks with high accuracy before they can cause harm. The reduction in costs and time for an IT team is worth the investment.

Learn more about building a cybersecurity prevention strategy in the white paper  Reinventing Cybersecurity Prevention with Deep Learning  from Deep Instinct.