Security Operations Center (SOC) analysts have it rough. Modern security tools generate an extraordinary number of alerts, attackers are more sophisticated than ever, and IT infrastructures are unprecedentedly complex. As a result, analysts are overwhelmed with workload and alerts, making it near-impossible to make intelligent, informed decisions. Fortunately, artificial intelligence (AI) is helping to ease the strain. Let’s look at how.
Better Allocated Resources
As noted, modern SOC analysts must deal with a barrage of security alerts. Not only do modern organizations suffer a vast number of attacks - a study from the University of Maryland found that the average computer is attacked 2244 times a day - but SOCs often try to detect every possible threat, seeking complete coverage of frameworks like MITRE ATT&CK without prioritizing based on their unique risk profile and existing security controls, and purchase new security tools without configuring or integrating them correctly. This approach to security generates a massive number of alerts, many of which are false positives or low-priority.
AI can help overcome this problem. Machine learning (ML) algorithms can analyze vast quantities of historical data and identify patterns to distinguish between genuine threats and false positives. Moreover, AI tools consider the environmental and historical context to filter out irrelevant or duplicate alerts and prioritize the most critical ones based on factors such as potential impact or likelihood of compromise. When alerts are triaged in this way, analysts can make more informed decisions about resource allocation, ultimately improving their ability to respond to threats and protect the organization.
Better Informed Incident Response
When a SOC receives an alert, analysts scramble to determine its legitimacy, cause, and necessary response actions. The faster and better informed this investigation is, the less likely the impact of a threat is to be. AI tools help inform incident response decisions by:
- Recommending Response Actions: AI can analyze contextual information to determine the type of threat and suggest appropriate response actions. For example, if AI detects a ransomware attack, it may suggest isolating affected systems, blocking malicious IP addresses, or locking down endpoints.
- Learning From Past Incidents: AI tools continuously learn from past incidents and outcomes, improving recommendations over time so analysts can better respond to future incidents.
- Responding to Incidents: Analysts can even configure AI tools to initiate response actions, effectively delegating decision-making responsibility and significantly reducing response times.
Automated Investigation, Liberated Analysts
SOC analysts spend a huge amount of time on repetitive, menial tasks. In fact, recent research published in Forbes revealed that 64% spend over half their time on tedious manual work.
The problem is that, upon receiving an alert, analysts must scramble across their tool infrastructure, manually gathering as much data as possible to make informed decisions. For example, a suspicious login alert may require an analyst to check user activity logs, verify access patterns, and cross-reference data.
AI tools can gather all this data in seconds. This speeds up investigation efforts and liberates analysts to spend their time on more strategic, nuanced decision-making tasks, such as proactive threat hunting, conducting route cause analyses, and developing tailored response strategies.
Integrated Threat Intelligence
AI-enabled SOCs can collate data from a vast number of threat intelligence sources, including public databases, threat-sharing communities such as ISACs, and internal feeds at incredible speeds. This data includes information on known threat actors, malware signatures, attack techniques, and malicious IPs or domains. By consolidating this data into a unified platform, AI ensures SOC analysts have the most up-to-date and accurate data to inform decision-making.
AI-Enabled SOC Considerations
However, while AI can have a transformative impact on decision-making in SOCs, empowering analysts to be more efficient, strategic, and accurate, it’s crucial to recognize that integrating AI into SOCs is no mean feat. Here are a few of the most important considerations:
- Trust and Explainability: AI tools must be highly transparent, and their decisions must be explainable - failing to ensure this can lead to inaccurate decisions that put your organization at risk and ethical concerns.
- Proper Integration: Failing to properly integrate AI into existing security infrastructure and workflows can create more problems than it solves. Look for solutions that integrate seamlessly without the need to replace existing tools.
- Security: AI tools must be secure, especially considering the enormous amount of data they process. Failing to secure them could result in data privacy regulation fines.
- Skills Gaps: Your existing analysts may not know how to use AI tools, so make sure you train them properly.
Ultimately AI is a powerful tool for SOCs, helping analysts to make intelligent decisions. While integration requires careful planning, its long-term benefits - greater efficiency, reduced workload, and improved security outcomes - far outweigh its drawbacks. AI-enabled SOCs are the future: act now to avoid being left in the past.
Editor’s Note: The opinions expressed in this and other guest author articles are solely those of the contributor and do not necessarily reflect those of Tripwire.
