What is Security Automation?
Security automation is the concept of using artificial intelligence (AI) and machine learning (ML) to proactively eliminate threats before they become a breach. Today’s bad actors use automation and AI to launch sophisticated, large-scale cyberattacks, particularly in complex, borderless IT environments (such as multicloud).
Together with cybersecurity consolidation, automation can help eliminate silos and prevent, detect, and respond to cyber threats without human intervention. Organizations can maximize the value of AI by using modern security orchestration technologies and processes to build strong defenses for today and tomorrow.
What Is Security Automation?
Security automation is the process of automatically preventing, detecting, identifying and eliminating cyberthreats. It can be effective even without human intervention but typically acts as a complement to the SOC team.
For example, AI for IT Operations (AIOps) harnesses big data from operational appliances across an organization. It then uses machine learning (ML) to detect patterns and relationships between those pieces of data, giving SOCs actionable insights to make decisions on security threats.
Modern cybersecurity automation solutions use AI and ML to protect an organization’s digital systems, programs, data, networks, applications and devices.
How Are Automation and Cybersecurity Related?
Security operations centers (SOCs) are traditionally run by analysts continuously scanning for breaches across the network. Analysts manually comb through threats—a time-consuming activity rife with high volumes of alerts, false positives, and distractions from larger security threats. The result is a perfect storm of overworked SOC teams, fewer results, and security gaps that lead to real breaches.
Automation eliminates many manual processes and reduces alerts, performing repetitive security tasks much faster for SOC analysts.
But just as security teams can use automation for their cyber resilience, malicious actors can also use automation for cyberattacks. Many of today’s cyberattacks use automation to scale quickly and use multiple attack methods to exploit vulnerabilities.
The reality is, manual processes simply can’t keep up with the volume of automated threats. That’s why organizations are increasingly adding cybersecurity automation to their defenses. In other words, fighting AI with AI.
Advantages of Using Automated Security Systems
1. Faster threat detection and response
Automated security systems can process massive amounts of data and uncover patterns that may be difficult for humans to recognize.
Let’s use cloud security as an example. Disparate security infrastructures across cloud and on-premises systems lead to thousands of alerts per day—with some incidents taking several days to investigate.
With automation, those cloud security alerts turn into automatic actions. Event data is analyzed and sent to data lakes, insecure cloud configurations are patched, and case management workflows are automated.
Cloud incidents are resolved automatically without any human intervention and at a speed much faster than typical security analysts.
2. Reduced likelihood of human error
Security analysts are often overwhelmed and overworked by the sheer volume of incidents that require attention. That leads to human errors. In fact, more than 74% of breaches involved human error, according to the 2023 Verizon Data Breach Investigations Report.
Cybersecurity automation eliminates many tedious and repetitive tasks typically given to analysts and provides deep insights that help in decision-making.
3. Increase operational efficiency
Security automation extends beyond just manual SOC responsibilities. Security teams often grapple with misconfigurations or siloed data from infrastructure that are rarely integrated, which makes changes prone to errors and slow down operations.
For example, a security team might receive several rule change requests to your network security policy per day—each taking hours or days to do. Those changes can be complex and cause application outages.
With automation, your team can customize workflows that automate the entire policy change process—from planning to validation and auditing. This eliminates the risk of human errors and minimizes any disruptions to your security team.
Examples of Security Automation Tools
1. Extended detection and response (XDR)
Extended detection and response (XDR) extend traditional EDR tools to any data source, including multicloud, networks and endpoints. XDR systems use heuristics, analytics, modeling and automation to reduce the time it takes to discover, hunt, investigate and respond to a threat.
2. Security orchestration, automation and response (SOAR)
SOAR tools help coordinate, execute and automate tasks between people and tools within an integrated cybersecurity orchestration platform. A SOAR solution typically includes threat and vulnerability management, security incident response and security operations automation.
3. Vulnerability management
Vulnerability management refers to a set of tools and processes that automate identifying, evaluating and remediating vulnerabilities. Vulnerability management includes automated assessment scans and reports, attack surface management tools and integration with SOAR.
AIOps analyzes large amounts of data to automate decisions. For example, with NetOps, AI can analyze network health data and provide network change teams detailed insights into how to improve their entire network.
How Does Cybersecurity Consolidation Impact Automation?
Traditional cybersecurity defenses have a hard time keeping up with today’s AI-based attacks. Organizations have to fight fire with fire—or AI with AI.
But to properly incorporate AI and automation into your cyber defenses, security tools need high volumes of data collected from across your infrastructure. That means data elements from across your network, cloud, operations and endpoints.
The data also must be consistent across all touchpoints in formatting, structure and labeling. This aggregated data allows your security automation to recognize and prevent attacks with or without the help of analysts.
That’s where cybersecurity consolidation comes in. With cybersecurity consolidation, data elements from your entire infrastructure are collected in one central data lake. Tools share the same intelligence and data, which allows AI algorithms to become more accurate when detecting and responding to future threats.
AI capabilities can isolate threats by user, device or location and initiate appropriate notification and escalation measures. At the same time, human experts can determine how to investigate and remediate.