AI as a Force Multiplier for Detection Engineering and Incident Triage

In modern enterprise SOCs, the sheer volume of security events has made it nearly impossible for human analysts to detect and respond to every potential threat. On a daily basis, we see security teams try to cope with thousands - if not tens of thousands - of “alerts” while simultaneously balancing prioritization, incident response, and other tasks. Effective detection engineering - the art of developing and refining threat detection rules - is more critical than ever. However, it remains a resource-intensive process that unfortunately many teams cannot address, and SOCs get stuck in a cycle of alerts that don’t matter with too little time.

The introduction of AI-driven solutions for SOC teams helps relieve this pressure. By automating routing tasks, enhancing the precision of detections, and enabling proactive security measures, AI acts as a force multiplier for detection engineers. Instead of drowning in an overwhelming volume of alerts, analysts can leverage AI to surface high-fidelity threats more efficiently. This shift not only improves detection accuracy but also allows SOC teams to allocate their expertise towards complex investigations and honing detections.

Automating Routine Tasks

One of the most impactful benefits of AI in detection engineering, and subsequently response, is the ability to handle repetitive, data-heavy tasks. This frees up analysts to focus on higher-level problem solving. 

Log Analysis and Correlation

Traditional log analysis - admittedly the bane of many SOC analysts - requires sifting through massive data sets to identify anomalous patterns. While we have systems - data lakes, SIEMs, correlation platforms, etc. - to assist with this, AI can help automate this process. Furthermore, AI can automate correlation across multiple data sources, highlighting unusual behaviors and anomalies without requiring predefined signatures.

The key takeaway: Time-intensive operations no longer steal minutes or hours from analysts; this frees them up to focus on crafting better detections across wider telemetry sets.

Incident Triage

AI-drive SOC solutions help prioritize investigations based on real risk factors, which reduce the noise from low-priority and informational alerts. Machine learning models assess historical data to determine which investigations require attention and which can be automatically resolved or deemed benign. Automatic risk consideration and models trained adapted to your organization allows for efficient incident triage.

The key takeaway: Only a small percentage of investigations actually need triage. AI-driven solutions help drive the threats that matter to the top, while automatically classifying lower priority incidents.

Automated Detection Rule Generation

The power of AI-driven SOC solutions, especially when it comes to detection engineering for enterprise teams, is in their ability to suggest new detection rules based on emerging attack techniques or observed environment-specific behavior. Furthermore, instead of manually honing every detection rule, engineers can leverage AI models to examine the history of data correlation and incident triage to refine detection logic based on real-time insights and intelligence.

The key takeaway: AI can assist in automating detection rule generation and honing, based on observations during routine task handling and data correlation. Engineers can lean on insights from AI-driven SOC solutions to find where detections can be further refined in their environment.

Enhancing Threat Detection Accuracy

With routine tasks handled efficiently, detection engineers can focus on the cornerstone of effective detection engineering: accuracy. Traditional rule-based detection methods often struggle to differentiate between benign anomalies and genuine threats, leading to false positives and false negatives - both of which can undermine security operational efficiency.

Let’s examine a few key areas where AI-driven SOC solutions enhance detection accuracy:

Behavioral Analytics‍

AI-powered behavioral analytics moves beyond simple signature-based detections, allowing security teams to detect subtle deviations from normal behavior. This is particularly useful for identifying insider threats and/or account takeovers, where attackers may have legitimate credentials.

Advanced Malware Detection‍

AI models trained on vast datasets can now recognize new, previously-unseen malware strains by analyzing how they behave rather than relying on known signatures. This approach has proven particularly effective in combating polymorphic malware, fileless attacks, and lesser-known files. 

Reducing False Positives

False alarms drain valuable time from security teams. AI continuously refines detection models, using reinforced learning to improve accuracy over time. This allows detection engineers to spend less time investigating benign events and focus on more real threats.

AI-driven SOC solutions help significantly improve signal-to-noise ratios, ensuring that teams are freed up to focus on the higher-level tasks.

Case Study: Reducing Alert Overload from PowerShell

Living-off-the-land binaries, or “lolbins”, and built-in scripting interpreters present a unique challenge for detection rules. These applications are used daily by the operating system and legitimate third-parties. Rules that are too broad generate far too many false positives, leading to alert overload and stress. Rules that are too narrow may miss adversary activity, while still generating false positives due to system behavior. Oftentimes, the key difference is in the parameters of the execution, or sometimes even the content.

The Problem

A large financial services SOC was drowning in PowerShell-related alerts, with thousands of executions logged daily. The majority of these alerts were Benign - flagging on legitimate system activity and third parties executing their scripts. PowerShell is a legitimate administrative tool, so we cannot simply “block” all activity. It is also commonly abused by adversaries for executing fileless malware, downloading payloads, and conducting reconnaissance.

Initial PowerShell detection rules are often too broad, generating significant alert volume for the SOC. Even a 50/50 split (50% Benign vs. 50% Inconclusive) can cost the team hours in triage and response. A few false positives we commonly see:

• Encoded PowerShell commands are not always malicious, and often used by system administration and security tools.
• Legitimate IT scripts running PowerShell were indistinguishable from adversary behavior in raw logs.
• Threat actors blended into normal PowerShell usage, making it difficult to refine detection rules without suppressing real threats.

The above situation puts SOCs in a constant reactive loop - drowning in alerts and unable to fine-tune detection logic effectively.

Refinement with AI-Driven SOC Solutions

To break the cycle, the SOC implemented AI-driven alert triage and behavioral analysis. This provided smarter, context-aware detection of truly malicious PowerShell activity.

Rather than treating all PowerShell activity as suspicious, AI models can analyze execution patterns, parent-child process relationships, and encoded command behavior to filter out routine administrative use. Examples may include:

• Use of suspicious execution flags coupled with other risk factors.
• Unusual parent-child process relationships, when compared to overall organization execution.
• PowerShell connectivity to external IPs or downloading of remote scripts, and
• Encoded commands linked to obfuscation techniques, ensuring that legitimate security tools weren’t automatically flagged.

With AI-driven improvements in their detection logic and investigation triage, the SOC automatically suppressed 65% of routine PowerShell alerts, with true positive activity quickly rising to the top.

The Outcome

With AI handling alert triage and offering suggestions to refine PowerShell detections, the SOC was able to reduce false positives and simultaneously find true positive behavior faster.  This led to key benefits such as:

• More precise detection logic - focusing on specific PowerShell executions linked to known adversary behavior.
• Reduction in false positives ensuring that SOC analysts focused on real threats rather than IT or third-party noise.
• Improve threat hunting efficiency as AI-driven filtering and triage allowed analysts to zero in on malicious PowerShell usage without being buried in low-value alerts.

Conclusion

The evolution of AI-driven tools continues to refine SOC analysis, incident triage, and detection engineering. What were once manual, reactive processes can quickly become adaptive and automated proactive processes. Leveraging AI as your team’s force multiplier, security teams can detect threats faster, reduce false positives, and focus on high-value investigative work.

However, let’s be clear on one thing: AI is not a replacement for skilled analysts and detection engineers. It is an enhancement that empowers. The most effective security teams will be those that strike the right balance between AI-driven SOC automation and human expertise, ensuring that teams remain agile in the face of evolving threats.

Further reading

• SOC metrics that matters
• What is an autonomous SOC? Can you build one today?
• Reducing alert fatigue, burnout, and SOC attrition with Agentic AI
• Agentic AI vs Security Copilots
• What is MFA fatigue attack?
• Investigating geo-impossible travel alert
• Top 3 scenarios for auto remediation
• Automated incident response: streamlining your SecOps
• Key SOC tools every security operations needs
• Demystifying SOC automation
• Alert triage and investigation in cybersecurity: best practices
• SOC challenges for analysts and managers
• Alert tuning best practices: keys to reducing false positives
• How to investigate Okta alerts
• AI SOC Analyst: A comprehensive guide
• Will AI replace cybersecurity jobs?

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