4 Ways Cybersecurity Automation Should Be Used
Cyberattacks have become heavily automated. Automation is the only way to reduce the volume of threats and enable faster prevention.
Modern cyberattacks have become heavily automated. If organizations try to defend against these attacks manually, the fight becomes man versus machine, with highly unfavorable odds for the organization. To successfully protect against automated attacks, it is essential to fight fire with fire – or in this case, machine with machine – by incorporating automation into cybersecurity efforts. Automation levels the playing field, reduces the volume of threats, and allows for faster prevention of new and previously unknown threats.
Many security vendors look at automation as a way to become more efficient and as a means to save in manpower or headcount. While true, automation should also be viewed as a tool that can, and should, be used to better predict behaviors and execute protections faster. If implemented appropriately and with the right tools, automation can aide in the prevention of successful cyberattacks. The following are four ways automation should be used:
1. Correlating Data
Many security vendors collect substantial amounts of threat data. However, data provides little value unless it is organized into actionable next steps. To do this effectively, organizations first need to collect threat data across all attack vectors and from security technologies within their own infrastructure, as well as global threat intelligence outside of their infrastructure.
Then, they need to identify groups of threats that behave similarly within the massive amounts of data and use that to predict the attacker’s next step. When using this approach, more data collected results in more accurate results, and reduces the likelihood that the groups identified merely an anomaly. Consequently, the analysis must also have enough computing power to scale today’s threat volume—something that is impossible to do manually. Machine learning and automation allow data sequencing to happen faster, more effectively, and more accurate. Finally, combining this approach with dynamic threat analysis is the only way to accurately detect sophisticated and never-before-seen threats.
2. Generating Protections Faster Than Attacks Can Spread
Once a threat is identified, protections need to be created and distributed faster than an attack can spread throughout the organization’s networks, endpoints, or cloud. Because of the time penalty that analysis adds, the best place to stop the newly discovered attack is not at the location where it was discovered but at the attack’s predicted next step. Manually creating a full set of protections for the different security technologies and enforcement points capable of countering future behaviors is a lengthy process that not only moves slowly but also is extremely difficult when correlating different security vendors in your environment and not having the right control and resources. Automation can expedite the process of creating protections without straining resources, all while keeping pace with the attack.
3. Implementing Protections Faster Than Attacks Can Progress
Once protections are created, they need to be implemented to prevent the attack from progressing further through its lifecycle. Protections should be enforced not only in the location the threat was identified, but also across all technologies within the organization to provide consistent protection against the attack’s current and future behaviors. Utilizing automation in the distribution of protections is the only way to move faster than an automated and well-coordinated attack, and stop it. With automated, big data attack-sequencing and automated generation and distribution of protections, you are more accurately able to predict the next step of an unknown attack and move fast enough to prevent it.
4. Detecting Infections Already in Your Network
The moment a threat enters the network, a timer starts counting down until it becomes a breach. To stop an attack before data leaves the network, you have to move faster than the attack itself. In order to identify an infected host or suspicious behaviors, you must be able to analyze data from your environment backward and forward in time, looking for a combination of behaviors that indicate a host in your environment has been infected. Similar to analyzing unknown threats attempting to enter the network, manually correlating and analyzing data across your network, endpoints, and clouds is difficult to scale. Automation allows for faster analysis and, should a host on your network be compromised, faster detection and intervention.
Attackers use automation to move fast and deploy new threats at breakneck speeds. The only way to keep up and defend against these threats efficiently is to employ automation as part of your cybersecurity efforts. A next-generation security platform rapidly analyzes data, turning unknown threats into known threats, creating an attack DNA, and automatically creating as well as enforcing a full set of protections through the organization to stop the attack lifecycle.
Learn more about the Palo Alto Networks next-generation security platform and how to prevent against advanced attacks here.