Q&A
How AI Is Transforming SOC Efficiency with Security Copilot
At Live! 360 Orlando, Microsoft MVP John O’Neill Sr. will explore how combining Security Copilot with Defender XDR is helping SOCs accelerate response times, improve accuracy and reduce analyst fatigue.
Security operations centers are under constant pressure to do more with less -- to process an ever-increasing volume of alerts, correlate incidents across hybrid environments and respond to threats faster than ever. As artificial intelligence becomes more tightly integrated into Microsoft’s security stack, many organizations are seeing measurable gains in speed, accuracy and analyst productivity.
At the upcoming Live! 360 event in Orlando, attendees will have an opportunity to dive deep into how Microsoft Security Copilot and Defender XDR are reshaping SOC workflows. The session, titled "Using Security Copilot and Defender XDR to Streamline Your SOC," will explore real-world examples of how organizations are using AI-assisted tools to streamline investigations, improve accuracy and reduce mean time to respond (MTTR).
Leading the session is John O’Neill, Sr., Chief Innovation Officer at Azure Innovators and longtime Live! 360 presenter. O’Neill will share insights from SOCs already using Security Copilot alongside Defender XDR to automate analysis, summarize complex incidents and empower analysts of all skill levels to respond to threats more effectively. His session promises to deliver practical guidance for organizations looking to balance automation with human oversight and get the most out of their AI-driven defenses.
In the following Q&A, O’Neill discusses where SOCs are seeing the biggest efficiency gains, how AI is reducing alert fatigue and the best practices for measuring post-deployment success with Security Copilot.
Redmondmag: What are the top efficiency gains SOCs are seeing when combining Security Copilot with Defender XDR?
O'Neill: Here are the top three efficiency gains the SOCs I work with are seeing when combining Security Copilot with Defender XDR:
- Speed and Response Time Improvements: IT Pros working in security are up to 22 percent faster across their tasks, with organizations achieving a near 30 percent reduction in mean time to respond (MTTR) for security incidents. This dramatic acceleration comes from AI-powered incident summarization that provides attack stories and potential impact, saving significant analysis time. This eliminates much of the manual effort typically required to piece together complex incidents.
- Automated Analysis and Query Generation: Security Copilot combined with Defender XDR excels at transforming complex technical tasks into simple, natural language interactions. Security teams can now use a query assistant that converts their natural-language question into a ready-to-run KQL query, while script analysis enables IT pros to analyze hundreds of lines of code, interpreting them via natural language in mere minutes, drastically surpassing even advanced analyst skills in terms of both speed and expertise. This enables junior team members to perform advanced tasks that previously required senior expertise.
- Quality and Accuracy Gains: Beyond speed, the integration delivers superior accuracy with near-zero false positives, allowing the SOC team to focus their limited time and resources on responding to real cyberthreats. The unified platform approach means false positives are reduced significantly and the work required for advanced, multitouch investigations decreases by 85 percent. Additionally, routine documentation becomes much easier as incident reports are quickly generated with the click of a button, instantly delivering high-quality summaries ready to share, freeing analysts to focus on threat hunting and strategic security initiatives rather than administrative tasks.
How does this integration reduce alert fatigue and improve response prioritization?
The Security Copilot and Defender XDR integration dramatically reduces alert fatigue through AI-driven triaging that evaluates incoming incidents and provides real-time grade recommendations -- True Positive (TP), False Positive (FP) or Benign Positive (BP) -- based on historical data and threat patterns, drastically reducing the time needed for incident prioritization and ensuring that critical threats are handled first while minimizing incident fatigue. The platform intelligently correlates alerts into prioritized incidents using machine learning, with alert aggregation helping to reduce alert fatigue and letting analysts focus and take action on fewer alerts for the same event. Additionally, guided responses recommend actions in categories including triage (classifying incidents as informational, true positive, or false positive), containment, investigation and remediation, ensuring analysts spend their valuable time on genuine threats rather than chasing false positives, with the system already achieving 89 percent positive user response rates in production environments.
Could you share a case study where this combo helped identify a threat that would've otherwise been missed?
A great example comes from a case where Security Copilot helped analysts discover a connection between a Defender XDR credential theft incident and a related Microsoft Sentinel incident involving SAP data exfiltration. In this case, a SecOps analyst was investigating what appeared to be a routine credential theft alert from Defender XDR involving a sales employee. However, Security Copilot's correlation capabilities revealed that the stolen credentials had been linked to suspicious activity in their SAP environment, uncovering an incident involving an exfiltrated SAP file. Without the unified analysis, this connection between the two separate security tools would likely have been missed, allowing the attacker to potentially redirect business payments.
What limitations or risks still require human intervention in Copilot-assisted triage?
Despite its advanced capabilities, Security Copilot still requires significant human intervention in several critical instances. The system might generate stale responses if it isn't given the most current data through user input or plugins. Prompts outside the scope of security might result in responses that lack accuracy and comprehensiveness. Human oversight remains essential because if any AI incorrectly identifies legitimate traffic as malicious, it could cause workflow interruptions or false positives, requiring keeping a human-in-the-loop for critical actions and ensuring that investigations are transparent and fully explainable for rapid review. Also, Security Copilot might generate code, or include code in responses, which might potentially expose sensitive information or vulnerabilities when not used carefully. Responses might appear valid but might not actually be syntactically correct. This potential limitation requires SecOps analysts to always take precautions including rigorous testing and security vulnerability checks. Microsoft designed the Security Copilot user experience to keep humans at the center, ensuring that while AI accelerates routine tasks, complex decision-making and validation of technical outputs remain firmly under human control.
How should organizations train analysts to effectively interpret Copilot output?
Organizations should train analysts on the elements of effective prompting, which include specific, security-related information that you need (Goal), why you need this information or how you plan to use it (Context), the format or target audience you want the response tailored to (Expectations) and finally known information, data sources, or plugins Security Copilot should use (Source). Microsoft emphasizes being specific, clear and concise as much as you can about what you want to achieve.
What telemetry or KPIs best demonstrate post-deployment success?
The most critical KPIs for demonstrating Security Copilot success center on operational efficiency improvements, particularly Mean Time to Response (MTTR) reduction. Our main result is a 30.13 percent reduction in MTTR associated with Copilot adoption, with some studies showing using Copilot for Security improved security response time by up to 26 percent. Organizations should leverage Security Copilot's built-in usage monitoring dashboard that provides a comprehensive view into various data helping you keep track of security compute unit (SCU) usage, including the number of provisioned and overage units used, the specific plugins employed during sessions and the initiators of those sessions. Key capacity planning benchmarks include provisioning approximately 1 SCU per embedded experience, 4 SCU per standalone experience and 5 SCU per automation and/or promptbook while measuring usage across different analyst skill levels and investigation types.
Success measurement should extend beyond speed to also encompass quality improvements and user satisfaction. Security novices and analysts using Copilot for Security performed better and expressed more confidence in their work compared to those who didn't use it, with organizations reporting considerable time savings in capturing and consolidating attack data, considerably reducing the time spent on these tasks. Organizations must actively monitor feedback metrics, as feedback is vital to guide the current and planned development of the product. Tracking positive feedback rates and the frequency of "Looks right" versus "Needs improvement" responses to gauge output accuracy and user satisfaction is another highly beneficial KPI. Additional quality-centric indicators include analyst skill advancement, reduced alert fatigue, and improved incident investigation quality, all of which contribute to demonstrating the system’s transformative impact on SOC operations.