Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Financial services can’t rely on manual review alone. Discover how unified data and explainable AI are helping firms detect risk, reduce cost, and stay ahead of evolving regulations. Financial services organizations are drowning in data. From emails and Bloomberg chats to WhatsApp messages and calls, the need to review communications data to detect potential misconduct and financial crime by employees and third parties is a mandated regulatory requirement for compliance and risk teams in 2025.

The 2025 State of Detection Engineering at Elastic explores how we create, maintain, and assess our SIEM and EDR rulesets. Today, Elastic Security Labs is releasing the 2025 State of Detection Engineering at Elastic! This brand new report is the first of its kind — we’re pulling back the curtain on our Detection Engineering practices, going beyond the traditional survey-style State of Detection Engineering report.

When an EDR tool generates too much endpoint telemetry, security teams quickly run into problems. Mountains of process events, network connections, and file operations can overwhelm analysts, making it harder to spot real threats in the noise. High data volumes drive up storage costs, slow down searches, and contribute to alert fatigue — leading to longer investigation times and potential blind spots.

A guide to aligning with SEBI’s CSCRF using Elastic's integrated security and observability capabilities Financial institutions in India are preparing for a new era of cybersecurity compliance with the Securities and Exchange Board of India’s (SEBI) Cybersecurity and Cyber Resilience Framework (CSCRF).

Stealthy adversaries continually exploit system utilities to execute malicious code. A particularly potent and frequently misused tactic is MITRE ATT&CK T1059 - Command and Scripting Interpreter, wherein attackers harness built-in interpreters like PowerShell, Bash, Python, or JavaScript to run arbitrary commands.

A practical guide for chief AI officers and technology leaders implementing federal AI governance The US Office of Management and Budget's recent memoranda — M-25-21, "Accelerating Federal Use of AI through Innovation, Governance, and Public Trust," and M-25-22, "Driving Efficient Acquisition of Artificial Intelligence in Government" — establish comprehensive frameworks for federal agencies that implement AI systems while maintaining appropriate safeguards.

Customizing and updating prebuilt SIEM detection rules just got easier, improving precision, enabling broader coverage, and saving time. Customizing and updating prebuilt detection rules is now easier than ever with Elastic Security. We’ve streamlined detection engineering workflows and enabled greater use case coverage with out-of-the-box SIEM detection rules.

Enhance your threat detection, investigation, and response by integrating Microsoft Defender for Endpoint data with Elastic Security. Many security teams often find it difficult to detect and respond to threats because of fragmented visibility and isolated endpoint data. This challenge led to the development of extended detection and response (XDR), which integrates endpoint insights with contextualized data from networks, cloud environments, and identity systems.

Quantifying the economic impact of Elastic Security As public sector organizations grapple with a changing economic and political landscape, the focus has increasingly turned to driving efficiencies, reducing costs, and strengthening resiliency.

Thriving organizations maximally allocate resources. With seemingly infinite cybersecurity threats and finite resources, everyone needs to know the size of the threat to determine priority, and where to invest to maximize ROI. Elastic takes a quantified approach to cybersecurity risk management using FAIR to break threat scenarios into (A) likelihood and (B) losses to calculate risk per year, AKA annualized loss expectancy, or in FAIR terms, simply “risk”.