SIEM

From MTTD and MTTR to Real Value: How to Organize SOC Metrics Effectively

Anatoly Antipov, head of L1 analysts at a small in-house SOC, explains why traditional time-based metrics like MTTD and MTTR often lead to superficial incident handling and analyst burnout. Drawing on NIST SP 800-61 and the latest SANS SOC Survey, the article shows how speed-focused KPIs encourage analysts to game the system rather than improve security. The team replaced vague verdicts with a five-level matrix including TP.Ext, TP.Int, BP, FP, and FP.SOC to separate real incidents, benign activity, and internal detection debt. Weekly reports were restructured around three blocks covering overall volume, verdict distribution, and confirmed violations with actual effort metrics. Regular quality audits of closed alerts now check verdict accuracy, documentation completeness, and whether FP.SOC items trigger rule improvements. The approach helps small SOC teams focus on genuine risk reduction instead of dashboard optics.

FSTEC Publishes 35-Point Network Perimeter Recommendations, Most Require No Spending

Russia's FSTEC released non-binding recommendations on protecting network perimeters on March 10, 2026, analyzing real-world intrusion vectors from external networks. The 35-point checklist spans eight sections covering device administration, DDoS resilience, segmentation, configuration backups, vulnerability management, authentication, logging, and incident response exercises. Only four items explicitly require purchasing new tools such as WAF, ZTNA, NAC, and SIEM, while five others depend on existing infrastructure. Twenty-six points can be addressed through policy, inventory, and configuration changes alone, including enforcing unique passwords, disabling legacy protocols like HTTP and SNMP v1/v2, and quarterly backup restoration tests. The document references Order 117 and earlier FSTEC methodologies on vulnerability handling, serving as a practical self-assessment questionnaire rather than a mandatory regulation. Analysts note that many organizations still fail to implement low-cost measures such as excluding remote administration interfaces from DMZ zones and verifying backup recoverability.

Building Information Security Culture: How Welcome Training Turned Rules into Engaging Dialogue

A large software development company transformed its approach to information security awareness by replacing formal policy sign-offs and portal documents with an interactive Welcome Training program. The 45-minute in-person sessions target developers, analysts, testers, product managers, and designers, focusing on real-world context, attack mechanics, and personal relevance rather than prohibitions. Training covers global and local threat landscapes, password policies, corporate email usage, sensitive data storage with VeraCrypt, secure credential sharing via pbin, file verification with VirusTotal, and social engineering defense. It also highlights existing corporate tools including Kaspersky Endpoint Security, Kaspersky Secure Mail Gateway, and SIEM systems to emphasize layered protection. The format has increased engagement, improved retention of guidelines, fostered conscious compliance, and noticeably reduced incidents stemming from human error. The company stresses that technology alone fails without employee understanding of why rules matter.

AI in Cybersecurity: Where It Delivers Real Value and Where It Remains Marketing Hype

The article examines how artificial intelligence is applied in information security, distinguishing genuine technological capabilities from vendor marketing claims. It explains the differences between classical correlation rules in SIEM systems, machine learning models for anomaly detection, and generative AI for analyst assistance. Real-world examples from Alfa-Bank highlight both successes in anti-fraud and UEBA systems and limitations when context or business understanding is required. The piece warns against inflated expectations that AI alone can replace SOC analysts or automatically investigate complex incidents. It concludes by identifying areas where AI genuinely reduces noise and processes large data volumes effectively.