Best Practices for Logging Data

Dear IT Auditors, Logging and observability for assurance You cannot audit what you cannot see. Logs tell the real story of system behavior. Leaders rely on them during incidents, investigations, and regulatory reviews. Your audit tests whether visibility exists when it matters most. You move beyond log existence. You test coverage, quality, and use. 📌 Identify critical systems and events You define which systems drive business risk. You list events that must be captured. Access attempts, configuration changes, data movement, and model updates all have an impact. You confirm that teams agree on what requires logging. 📌 Test log completeness You verify logging is enabled across environments. You confirm no critical components run without logs. You test for gaps during peak periods. You flag the blind spots that attackers exploit. 📌 Review log integrity You check time synchronization. You confirm logs are immutable. You review access to logging systems. You identify risks of tampering or deletion. 📌 Validate retention and storage You test retention periods against legal and internal requirements. You confirm that storage protects confidentiality. You flag logs deleted too early or stored without encryption. 📌 Inspect monitoring and alerting You review alerts tied to high-risk events. You test if alerts trigger action. You confirm ownership and response times. You identify noise that hides real threats. 📌 Trace incidents to logs You select recent incidents or control failures. You trace the investigation steps back to the logs. You confirm that teams used logs effectively. You highlight cases where missing data slowed response. 📌 Assess cross-system correlation You review how teams correlate logs across platforms. You test visibility across cloud, on-prem, and third-party services. You flag siloed monitoring. 📌 Close with assurance-focused conclusions You show leaders where visibility supports trust. You highlight gaps that weaken assurance. You provide clear actions to improve observability fast. #ITAudit #CybersecurityAudit #Logging #Observability #InternalAudit #GRC #CloudSecurity #RiskManagement #ITGovernance #IncidentResponse #TechLeadership

🌍International Guidance for Enhanced Cybersecurity: Best Practices for Event Logging and Threat Detection🌍 The Australian Government's Australian Cyber Security Centre (ACSC), in collaboration with global partners like the #NSA, #CISA, the UK's #NCSC, and agencies from Canada, New Zealand, Japan, South Korea, Singapore, and the Netherlands, has released a comprehensive report on best practices for event logging and threat detection. 🚀The report defines a baseline for event logging best practices and emphasizes the importance of robust event logging to enhance security and resilience in the face of evolving cyber threats. Why Event Logging Matters: Event logging isn't just about keeping records—it's about empowering organizations to detect, respond to, and mitigate cyber threats more effectively. The guidance provided in this report aims to bolster an organization’s resilience by enhancing network visibility and enabling timely detection of malicious activities. 🔍 Key Highlights: 🔹Enterprise-Approved Event Logging Policy: Develop and implement a consistent logging policy across all environments to enhance the detection of malicious activities and support incident response. 🔹Centralized Log Collection and Correlation: Utilize a centralized logging facility to aggregate logs, making detecting anomalies and potential security breaches easier. 🔹Secure Storage and Event Log Integrity: Implement secure mechanisms for storing and transporting event logs to prevent unauthorized access, modification, or deletion. 🔹Detection Strategy for Relevant Threats: Leverage behavioral analytics and SIEM tools to detect advanced threats, including "Living off the Land" (LOTL) techniques used by sophisticated threat actors. 📊 Use Case: Detecting "Living Off the Land" Techniques: One highlighted use case involves detecting LOTL techniques, where attackers use legitimate tools available in the environment to carry out malicious activities. The report showcases how the Volt Typhoon group leveraged LOTL techniques, such as using PowerShell and other native tools on compromised Windows systems, to evade detection and conduct espionage. Effective event logging, including process creation events and command-line auditing, was crucial in identifying these activities as abnormal compared to regular operations. Couple this report with the CISA Zero Trust Maturity Model (ZTMM): The report's best practices align with CISA's ZTMM's Visibility and Analytics capability. By following these publications, organizations can progress along their maturity path toward optimal dynamic monitoring and advanced analysis. (Full disclosure: I was co-author of CISA's ZTMM) 💪Implementing these best practices from the Australian Signals Directorate & others is critical to achieving comprehensive visibility and security, aligning with global cybersecurity frameworks. #cybersecurity #zerotrust #digitaltransformation #technology #cloudcomputing #informationsecurity

How Removing One Logger Made Our Spring Boot App 15x Faster 🫣⚡ Yes… a LOGGER. Not a query. Not a transaction. A single logger line slowed our entire service down. Here’s what happened 👇 We had a debug statement inside a high-throughput method: log.debug("Processing request data: {}", requestData); Seems harmless, right? But requestData was a huge object with multiple nested fields. Even with DEBUG disabled, Spring still had to: Build the log message parameter Convert the object to String Allocate buffers And only THEN decide not to print it So we were wasting CPU recreating giant strings millions of times per day. The fix? Use lazy logging with lambda: log.debug("Processing request data: {}", () -> requestData); 💡 Instant impact: CPU usage ↓ 40% GC pressure ↓ massively Request latency ↑ from 120ms → 8ms Throughput ↑ 15x during peak load All because the log message was never constructed unless DEBUG was actually on. 📊 Key takeaways: Even disabled logs cost CPU if parameters are heavy Always use lazy logging in hot paths Don’t log large objects in request/response flow Small fixes at high-frequency code paths = massive performance wins Profiling > guessing — 90% of bottlenecks aren’t where you think they are #SpringBoot #JavaPerformance #LoggingBestPractices #BackendEngineering #CodeOptimization #Microservices #JavaDevelopers #SoftwarePerformance #CleanCode #TechInsights

#Day108 Centralized Logging: Simplifying Troubleshooting and Observability Centralized logging is a game-changer for modern systems. Instead of hunting for logs across multiple servers or applications, it brings all your logs into one place for easy access and analysis. Why is Centralized Logging Important? 1. Faster Issue Resolution Imagine your application crashes during peak hours. Centralized logging lets you trace the issue in seconds, pinpointing errors or bottlenecks without manual digging. 2. Monitoring System Health With logs from servers, applications, and containers in one place, you get a holistic view of system performance and can detect anomalies before they become major problems. 3. Audit and Compliance Need to demonstrate compliance with security standards? Centralized logs create clear trails for audits, saving time and effort. Real-Life Use Cases • E-commerce Site During Sales During a flash sale, an e-commerce platform experienced a sudden spike in database errors. Using centralized logging, the engineering team identified slow queries caused by outdated indexes. Fixing it on the fly reduced cart abandonment rates. • Microservices Debugging In a microservices architecture, tracking issues across services is a nightmare. Centralized logging allows developers to trace requests end-to-end, connecting logs from different services to find the root cause. • Proactive Alerts A financial services company used centralized logging to set up alerts for unusual patterns, like repeated login failures. This early detection helped mitigate potential security breaches. How to Get Started • Tools to Explore: ELK Stack, Splunk, Fluentd, or Grafana Loki. • Log Consistency: Structure logs in formats like JSON to make them searchable. • Dashboards: Build simple dashboards to visualize trends and track anomalies in real time. • Retention Policies: Ensure logs are stored securely with appropriate retention timelines. Centralized logging isn’t just about managing logs—it’s about improving reliability, reducing downtime, and gaining valuable insights into your systems. #DevOps #CentralizedLogging #TechSimplified #Observability

Bad logs are just noise. Good logs lead you to a fix. Here are 7 Rules of Thumb for Effective Logging. 1. Use Structured Logging Format log entries structured to enable easy parsing and processing by tools and automation systems. 2. Include Unique Identifiers Each log entry should have a unique identifier (correlation IDs, request IDs, or transaction IDs) to trace requests across distributed services. 3. Log entries should be small, easy to read, and useful. Don't overload your logs with unnecessary info. Focus on what's important and make sure your logs are easy to read. 4. Standardize Timestamps Use consistent time zones (preferably UTC) and formats. Logs with mixed time zones or formats can turn debugging into a nightmare. 5. Categorize Log Levels • Debug: Detailed technical information for troubleshooting during development. • Info: High-level operational information. • Error: Critical issues requiring attention. 6. Include Contextual Information Contextual details make debugging easier: • User ID • Session ID • Environment-specific identifiers (e.g., instance ID) Context helps you understand not just what happened, but why and where it happened. 7. Protect Sensitive Information • Don’t log private data like passwords, API keys, or Personally Identifiable Information (PII). • If unavoidable, mask, redact, or hash sensitive data to protect users and systems. Many logging frameworks already support these features, so there's no need to reinvent the wheel. Use them, and life gets a whole lot easier. P.S. What's your favorite logging framework? Mine's Serilog

In this video, I explain the basics of application logging and why it’s one of the most important parts of modern software development. Logging involves more than just writing events to a file; it focuses on creating meaningful, structured insights that help developers and businesses debug, analyze, and improve systems. What you’ll learn in this video: - What logs are and why they matter - Different types of logs: event, transaction, and error/exception logs - Why logging is vital for debugging, auditing, and finding the root cause - The limits of traditional logging methods - The importance of structured logging and semantic logging - Best practices for writing effective, consistent, and meaningful logs - How tools like Logstash, Kibana, and Grok can turn unstructured logs into useful insights By the end, you’ll know how to create better logging strategies that not only help developers fix issues but also provide business value through data-driven insights. If you’re a software engineer, someone who designs systems, or anyone working with production systems, this session will offer practical tips to make your logging smarter and more effective. https://lnkd.in/gst5FKMC #krishdinesh #krishtalks #smarterbrandix #ApplicationLogging #StructuredLogging #SoftwareEngineering #BestPractices #Debugging #LogManagement #SemanticLogging #DevOps #SoftwareArchitecture #Monitoring #Logstash #Kibana #SoftwareDevelopment #TechTalk #CloudComputing

𝐌𝐚𝐱𝐢𝐦𝐢𝐳𝐞 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞: 𝐖𝐡𝐲 𝐌𝐨𝐝𝐞𝐫𝐧 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐌𝐚𝐭𝐭𝐞𝐫 Mastering logging frameworks is essential for resilient, maintainable application development today. They enhance observability, ensure issues are caught early, and help teams analyze systems at scale for performance and reliability. 🔹𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤? ▪A structured toolkit for managing how logs are generated, formatted, filtered, and routed in applications.  ▪Standardized log levels (e.g., INFO, WARN, ERROR) provide clarity and facilitate efficient filtering and searching across systems. 🔹𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐟𝐨𝐫 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 ▪Modern frameworks provide structured formats like JSON, making automated analysis, alerting, and data extraction seamless.  ▪Structured logs are machine-readable, making integrations with monitoring and analytics tools much more reliable. 🔹𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧 ▪Flexible frameworks allow attaching specific context (such as user ID or transaction ID) to each log entry, improving diagnostic capabilities.  ▪Enriching logs with additional, relevant metadata (like environment or service name) supports end-to-end tracing and troubleshooting. 🔹𝐄𝐫𝐫𝐨𝐫 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫 ▪Good frameworks include full error context, such as stack traces, to expedite debugging and remediation.  ▪Descriptive log messages with clear error codes and relevant input details make root-cause analysis faster and more accurate. 🔹𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 ▪Efficient frameworks impose minimal overhead even under high load, preserving application performance.  ▪Asynchronous logging and choosing performant libraries are key to minimizing the impact of logging on response times. 🔹𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐨𝐬𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 ▪Sampling capabilities help reduce data transport and storage costs by retaining only necessary logs – for instance, keeping just 10% of logs in high-volume scenarios.  ▪Log rotation and retention policies should be tuned to control costs and ensure compliance without sacrificing troubleshooting data. 🔹𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐍𝐨𝐧-𝐈𝐧𝐭𝐫𝐮𝐬𝐢𝐯𝐞 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 ▪The right logging framework is non-intrusive, ensuring log statements do not alter application execution or test results.  ▪Logging should avoid leaking sensitive or personal data to maintain security and privacy, even in test environments. 👉Strong logging practices—powered by robust frameworks—are foundational for modern application success. 👉Select, configure, and iterate on logging with care to deliver resilient, transparent, and performant software in today’s demanding technical landscape. Have a great weekend, eveyone! 𝐒𝐨𝐮𝐫𝐜𝐞/𝐂𝐫𝐞𝐝𝐢𝐭: https://lnkd.in/guUbFfMu #AI #DigitalTransformation #GenerativeAI #GenAI #Innovation  #ArtificialIntelligence #ML #ThoughtLeadership #NiteshRastogiInsights 

The pipeline failed again. It was 2 AM. The ETL job had crashed — all because of one bad record. A missing date.  One null in a field it shouldn't have been.  And the entire pipeline? Stopped. That night, I learned a valuable lesson: Never let bad data block good data. Now I follow a simple rule: 1. Validate early 2. Load good data 3. Log the bad rows — with error reason, timestamp, and raw content 4. Fix them later, without breaking everything else You can’t avoid bad data. But you can try to avoid broken pipelines. Protect your sleep. Build pipelines that bend — not break. Future you — especially at 2 AM — will be grateful. Was this relatable? 👍 Tap like if you're on this journey too. 🔁 Share it — someone out there might need to hear this today. 🚀 Follow Sahil Alam for tips on SQL, Python, and breaking into data roles with confidence! #DataEngineering #ETL #ELT #DataQuality #dbt #Snowflake #Analytics #CareerLessons