All news with #security analytics tag

🔎 While reviewing five popular SIEM solutions for a security awards panel, the author observed consistent marketing claims—24/7 SOCs, AI-driven detections, integrations and SLA promises—but a notable omission: how vendors manage multi-tenancy. The piece explains the engineering risk of the “noisy neighbor” effect in shared cloud stacks and shows how poor isolation can produce ingestion latency, delayed detection and violated SLAs. It recommends concrete architectural controls—admission control, fair-share scheduling and resource partitioning—and urges buyers to demand transparency or opt for dedicated clusters when compliance or performance require strict isolation.

🕵️ GreyNoise analyzed 4 billion malicious sessions over three months and found residential proxies accounted for roughly 39% of traffic yet evaded IP reputation feeds in 78% of cases. Researchers say the short-lived, systematically rotated, or low-activity nature of these addresses prevents timely cataloging by reputation systems. They recommend moving from IP-based blocking to behavior-focused detection, such as spotting sequential probing and tracking device fingerprints that persist through IP rotation.

🔍 Modern SIEM platforms have evolved far beyond simple log collection, embedding AI/ML, XDR, and SOAR to enable real-time detection, automated remediation, and analyst workspaces. Convergence with XDR and SOAR is creating unified platforms that reduce complexity and accelerate response, while many SMBs opt for MDR instead of maintaining full SIEM deployments. Economic shifts and AI compute costs are changing cloud vs. on-prem trade-offs, and vendors are consolidating functionality through M&A and bundling.

🔒 Wazuh is an open-source SIEM and XDR platform designed to help organizations build proactive cyber resilience by delivering centralized visibility, continuous detection, and automated response across endpoints, servers, cloud workloads and containers. It collects telemetry via agents, syslog and agentless methods, enabling early detection through log analysis, File Integrity Monitoring and correlation rules. Automated response actions and AI-assisted analysis speed containment and remediation while vulnerability detection and security configuration assessments support ongoing IT hygiene and compliance.

🛡️A new Sumo Logic report finds widespread AI/ML adoption in security operations but limited depth of use. The 2026 Security Operations Insights study, published 28 January, shows 96% of security leaders report adopting AI/ML, with 90% valuing it for reducing alert fatigue and improving detection. However, most cited relatively basic use cases — threat detection, automated response, anomaly detection and incident triage — challenging vendor narratives about broad, deep AI integration. The survey also highlights tool sprawl and alignment gaps between security and DevOps.

🛡️ Organizations should rebuild security frameworks when they fail to sense environmental change, respond effectively to incidents, or support proactive risk management. Experts recommend a dynamic sensing-and-response capability, routine reviews (biannual heavy reviews with interim cursory checks), and deliberate integration of NIST baselines with industry-specific controls. Key warning signs include any breach, chronic alert overload, negative KRIs/KPIs, endpoint and AI gaps, and a compliance-only posture that ignores business risk. Rebuilds are also warranted after major business or regulatory shifts or when incremental fixes no longer suffice.

🔍 BigQuery now supports MATCH_RECOGNIZE, a SQL clause for identifying ordered patterns across rows and time-series data. It lets analysts express complex sequence logic—using PARTITION BY, ORDER BY, PATTERN, DEFINE and MEASURES—inside a single query without heavy joins or external processing. The feature targets use cases like funnels, fraud detection, log sequencing, and financial pattern detection, and is immediately available to all BigQuery users.

🔍 AWS Step Functions now provides a unified metrics dashboard in the console that centralizes usage and billing metrics for both account and state-machine levels. The dashboard covers standard and express workflows and surfaces existing metrics such as ApproximateOpenMapRunCount. It is available in all Regions where the service operates and can be opened from the Step Functions console.

🔍 Amazon Managed Service for Prometheus now includes anomaly detection using the Random Cut Forest (RCF) algorithm to continuously analyze time series and surface unexpected metric behavior with minimal user intervention. When you create an anomaly detector in an AMP workspace, it generates four derived time series that represent detected anomalies and their confidence values. Those derived series can be used to build dynamic alerting rules in the AMP Alertmanager and visualized alongside input metrics in self‑managed Grafana or Amazon Managed Grafana. The feature is available in all regions where AMP is generally available and is configurable via the AWS CLI, SDKs, or APIs.

🔍 This article explains how organizations can measure cybersecurity effectively by aligning technical metrics with executive concerns. It outlines five iterative steps — define requirements, select key indicators, identify metrics, collect and analyze data, and report indicators — to create an actionable measurement cycle. Emphasis is placed on using high-level KPIs and KRIs, automating collection, and reviewing indicators with stakeholders to ensure relevance and drive decisions.

📊 Measuring security performance is essential for CISOs who must demonstrate how security supports business objectives. The article outlines ten metric categories — including incident response (MTTD/MTTR), vulnerability "window of exposure," security awareness and maturity — and stresses choosing metrics that answer stakeholders' questions. Experts such as Richard Absalom and Frank Kim advise avoiding meaningless measurements and using metrics to prioritize work, allocate resources and communicate security value to the board.

🛡️ Organizations facing security-tool sprawl should begin by inventorying controls and eliminating those that no longer map to business risk. Use automated analytics and dashboards to surface ineffective or redundant products, and prioritize tools that enable automation to consolidate alerts and workflows. Remove duplicate solutions—often introduced through acquisitions or silos—and move toward unified platforms while fostering continuous training so teams actually use and benefit from deployed tools.

🔍 At .Conf in Boston, Splunk and parent company Cisco positioned machine data as central to next‑generation AI incident response, arguing telemetry represents roughly 55% of global data growth. They stressed tighter integration of security and observability, a federated data model with new support for Snowflake, and standards work such as OpenTelemetry and the Open Cybersecurity Framework (OCSF). Splunk also previewed enhanced security operations capabilities — a premier Enterprise Security bundle, Detection Studio, and agentic AI features — while acknowledging customer concerns about costs, legacy positioning, and support.

🔐 Avnet moved away from vendor-bound SIEM, EDR and RBVM silos toward a centralized security data pipeline built on Cribl, prompted by a legacy SIEM renewal that became a strategy inflection point. The redesign gave Avnet full ownership of telemetry, enabled large-scale ETL and flexible routing, and freed analysts from vendor dashboards. Operationally, licensing and storage costs dropped dramatically to 15% of prior levels while processing capacity doubled and pipeline staffing fell from four engineers to one. With its own data layer in place, Avnet is accelerating analytics and AI use cases such as tailored LLMs and retrieval-augmented generation (RAG) to improve investigations and reduce analyst workload.