Automated workflows  – Repetitive tasks like threat correlation and incident escalation can be automated Time-consuming and reactive Rapid, proactive response How does Splunk enhance threat detection and analysis By efficiently collecting, analysing, and correlating security logs, Splunk enables SecOps teams to detect Forensic investigation tools  – Teams can drill down into historical data for post-incident analysis. AI-driven, real-time alerts Investigation Time-consuming log searches Automated event correlation Response

engineers and security professionals, the consequences are serious, delays in triage, blind spots in analysis An IT engineer can correlate infrastructure performance logs with financial data to better understand analytics.  Efficiency Gains : Snowflake handles heavy analytics work; Splunk handles real-time correlation and alerting For professionals, it means being proficient in how Splunk connects, correlates and operationalises data

By providing continuous monitoring and analysis, security logs help businesses stay on top of security back to their origins, aiding in root-cause analysis and faster mitigation. Real-time analysis : Continuous log analysis enables teams to identify threats as they happen, allowing Data correlation : Log security tools can correlate data from different systems to provide a more comprehensive Data correlation : Correlating data from various sources and systems can be complex and time-consuming

global data volumes could reach around 181 zettabytes by 2025 , driven by AI-  With that scale, manual analysis It correlates signals across domains, detects anomalies at scale and supports orchestrated responses Faster analysis improves response times. Strong governance builds trust.

Why is root cause analysis critical in IT problem management? Root cause analysis (RCA) is the cornerstone of problem management. Reduces future incidents : Proper root cause analysis helps organisations prevent similar issues from Some of the most effective techniques for root cause analysis include: 5 Whys : Asking "why" five times Pareto analysis : Prioritising causes based on their impact.

Alert fatigue:  Security analysts are overwhelmed by the alerts and struggle to prioritise threats efficiently some key benefits: Reduced workload for security teams:  Automation alleviates the burden on security analysts Here are some key use cases: Incident response : Automatically detect, analyse, and respond to security Threat intelligence integration : Automatically ingest and correlate threat intelligence data to enhance Machine learning enhances threat detection accuracy by analysing data patterns and adapting to new threats

Root cause analysis:  Rather than just asking Splunk to identify security problems, Patrick focuses on Splunk has helped Patrick transform how he handles data in several ways: Real-time data analysis:  Splunk ’s ability to process and analyse data in real-time allows Patrick to quickly identify potential security Splunk, Patrick realised that as long as data can be ingested into the system, the possibilities for analysis Focus on root cause analysis:  Instead of just identifying problems, dig deeper into the root causes

Machine Learning : Almost all leaders are leveraging AI and ML to improve observability, helping teams correlate Leverage AI : Use AI-powered insights to enhance your ability to correlate events and prioritise critical

Unlike traditional automation, Agentic AI acts with autonomy, analysing, learning, and executing tasks In many Security Operations Centres (SOCs), analysts still face overwhelming alert volumes every day. Instead of toggling between fragmented tools, analysts can now manage their entire incident lifecycle Malware Reversal Agent – Automatically analyses malicious scripts line-by-line, flagging indicators Federation with Cisco Firewall Data – allows analysts to perform real-time analytics on firewall logs

Security intelligence involves collecting, analysing, and interpreting data related to potential security Example:  Splunk's integration with AI and ML allows organisations to automate data analysis, providing Threat analysis:  Evaluating data to identify potential threats. To understand related concepts in data monitoring and analysis, explore the purpose of Splunk Observability following best practices: Invest in the right tools:  Choose platforms like Splunk that offer robust data analysis

certifications are industry-recognised credentials demonstrating your expertise in using Splunk's powerful data analytics It opens up various job opportunities, such as Security Engineer, Data Analyst, and  Security Information This proficiency allows for more efficient data analysis, faster troubleshooting, and improved decision-making Splunk Certified Cybersecurity Defense Analyst:  Designed for analysts specialising in cybersecurity.

AI-assisted observability supports faster root-cause analysis, improved anomaly detection, and better It ensures teams can configure alerts, analyse telemetry data, and optimise performance at scale.