Why Does Machine Learning Matter in Cybersecurity?
Machine learning is changing the cybersecurity game, empowering network professionals to move from a reactive security posture to one that is proactive.
During the last two decades, network security experts have attempted to counter cyberattacks by shortening the amount of time it takes to identify and neutralize threats. Response times have shrunk from days to hours or minutes, but cyberattackers haven’t given up. If anything, cyberattacks have become more frequent and more sophisticated, with the potential to wreak havoc on businesses, government agencies and utilities in seconds.
Most security experts recognize that when it comes to cyberattacks, the industry has been playing defense for some time. However, with machine learning (ML) algorithms now used to detect network intrusions, malware and phishing attempts, security professionals have a potent new weapon at their disposal.
Intelligent Network Security
ML gives security experts and their organizations more control over their network security. Because ML can anticipate and fight threats in near-real time, network security becomes intelligent, moving network protection from a reactive state to a proactive one. Here is how:
- Stay ahead of emerging threats.
IT teams gain an advantage when they can switch from defense to offense against cyberthreats. When used at the core of the network, ML algorithms can identify both known and unknown threats. This is the case for ML-Powered Next-Generation Firewalls, which use ML inline to defend against 95% of unknown threats. Threats not blocked by inline machine learning can be neutralized in near-real time with zero-delay signature updates.
- Gain visibility and security for the entire enterprise.
Security teams can’t be effective without network visibility. Being able to see all applications, users and devices has become increasingly important as more Internet of Things (IoT) devices, such as cameras and tablets, are added to the network. ML-based security can offer end-to-end device visibility and help detect network anomalies.
- Improve security policies.
ML modeling can translate telemetry information into recommended security policy changes. This capability is particularly important for IoT security because it allows security professionals to review and adopt IoT security policy recommendations for all the devices in a network. The result is improved security for the enterprise and time savings for security teams.
- Reduce breaches caused by human error.
Network security professionals understand how challenging it can be to keep up with the rate of change in applications and devices. Additionally, updating security policies manually is often cumbersome and error-prone. When used at the core of network security, ML can recommend and propagate strong security policies, saving security teams hours of manual updates as well as reducing the chance of human errors.
Why Should Security Teams Consider Adopting an ML-Powered NGFW?
The ML-Powered NGFW disrupts the way security has been deployed and enforced so far. Security teams should consider adopting an ML-Powered NGFW because:
- Based on testing, it proactively prevents up to 95% of new threats instantly.
- It stops malicious scripts and files without sacrificing the user experience.
- It extends visibility and protection to IoT devices without additional hardware. Based on customer data, the number of detected IoT devices increases by a factor of three.
- It reduces human error and automates security policy updates to prevent the most advanced attacks.
Want to learn how Palo Alto Networks is leveraging machine learning to protect today’s enterprises from tomorrow’s threats? Read our e-book 4 Key Elements of an ML-Powered NGFW: How Machine Learning Is Disrupting Network Security.