A SOC’s effectiveness isn’t just measured by its ability to detect threats—but also by the accuracy and reliability of the alert triage process. At Intezer, we’ve developed a scientific quality assurance methodology for our AI SOC. Each week we undertake a rigorous validation process to ensure consistent performance, while maintaining complete transparency with our customers.
Step 1 – Alert Validation
Our process begins with systematic alert validation through two complementary approaches:
- Random Sampling: Our expert security analysts evaluate a random selection 5% of all alerts processed by our system. This allows us to identify patterns, detect edge cases, and continuously refine our algorithms with statistical significance.
- User Feedback Integration: We incorporate direct feedback from the security professionals who use our platform every day. When users approve or reject triage verdicts using our intuitive “Thumbs Up/Thumbs Down” feature, they contribute valuable real-world validation that helps our system learn and improve.
This dual, human-in-the-loop validation process ensures we maintain a 95% confidence level in our quality measurements with a margin of error less than 2%.
Step 2 – Establishing Performance Metrics
The data collected through our alert validation process feeds into our weekly performance benchmarking. This allows us to establish and monitor critical KPIs with scientific rigor:
- Escalation Rate: We track the percentage of alerts that our AI SOC routes back to your team for further analysis. Our 2024 benchmark shows an impressive 3.81% escalation rate, demonstrating our ability to significantly reduce workload while flagging critical threats.
- Accuracy: We monitor both False Positive and True Positive accuracy to prevent irrelevant alerts from burdening your team and ensure legitimate threat identification.
- Average Investigation Time: We measure average and median investigation times to maintain efficiency and consistency.
- Definitive Classification Rate: This unique metric measures how frequently our system is able to reach conclusive verdicts, helping us reduce the number of alerts requiring manual review.
See how our AI SOC performed in 2024: 3 Critical Metrics for Evaluating AI SOC Solutions.
Step 3 – Feedback Loop
Our commitment to excellence extends beyond validation and metrics through a comprehensive feedback loop that drives continuous improvement:
- Pattern Recognition: We meticulously analyze every error, looking for trends and patterns in misclassifications or suboptimal assessments. This helps identify common failure modes and prioritize improvements.
- Impact Analysis: Rather than making arbitrary changes, we evaluate potential improvements based on their projected impact on overall system performance to deliver the greatest functionality to users.
- Iterative Refinement: Based on our analysis, we implement targeted improvements to our algorithms, models, and decision-making processes. These changes are then continuously validated to create a virtuous cycle of improvement.
- Environment-Specific Adaptation: We integrate environment-specific rules and exceptions and privately index relevant software code to fine-tune our system for each customer’s unique environment, enabling accurate assessment of custom solutions.
Intezer’s Scientific Advantage
Our approach to quality assurance represents a fundamental shift in how AI-powered security solutions should be evaluated. Unlike black-box solutions that obscure the decision-making process, Intezer believes in complete transparency. Our integrated approach to validation and benchmarking allows us to:
- Share detailed performance metrics with customers derived directly from our validation process
- Provide clear explanations for alert classifications
- Demonstrate continuous improvement through measurable results
- Build trust through verifiable processes rather than marketing claims
With Intezer’s quality assurance cycle in place, we provide the confidence modern security operations demand in an industry where false positives waste valuable time, and false negatives lead to catastrophe.
Reach out today for an inside look at Intezer’s Autonomous SOC.
The post Quality Assurance in Intezer’s Autonomous SOC: A Scientific Approach to Excellence appeared first on Intezer.