Alarm System Optimization

Still trying to manage your ever-increasing alert flow by hiring more analysts? That’s much like adding buckets to deal with a leaking roof. Invest in detection engineering and automation engineering to reduce the alert flow and prevent alert fatigue and unhappy analysts. Here are some best practices: - Apply an automation-first strategy: handle and/or accelerate all alerts through automation - Continuously tune and optimize detection rules - Let analysts and detection / automation engineers work closely together to increase the effectiveness of engineering efforts - Establish metrics for rule quality to identify candidates for tuning and automation - Test against defined quality criteria before putting any detection rules live - Increase the fidelity of your rules by alerting on more specific criteria - Aggregate and analyse batches of noisy alerts daily or weekly, instead of handling them individually in real-time - Consider your ideal ratio between analysts and engineers. Start out with 50-50, then decide what would best suit your needs - Make risk-based decisions on added value of rules compared to time investment, and drop time-consuming rules with little added value if they cannot be tuned properly This is by no means an easy thing to do. But by focussing on engineering and detection quality, you can transition to a state where you control of the alert flow instead of the other way around, so that analysts can focus on the alerts that truly matter. #soc #securityoperations #securityanalysis #detectionengineering #automationfirst

In the fast-paced world of cybersecurity, alert storms can overwhelm Security Operations Centres (SOCs), causing analyst fatigue and increasing the risk of critical threats slipping through unnoticed. Managing these storms effectively is crucial to maintaining operational stability and protecting sensitive data. 5 WAYS TO AVOID ALERT STORMS IN SECURITY OPERATION CENTRE (SOC) 1. UNIFY THREAT MONITORING Fragmented security tools generate isolated alerts, leading to duplicate notifications and poor threat correlation. By unifying threat monitoring across systems, you can: • Centralise all alerts from firewalls, SIEMs, EDR and other tools in a single platform. • Streamline threat visibility to identify patterns across multiple attack vectors. • Reduce manual effort and improve incident prioritisation. Example: Use a well-integrated SIEM solution to ingest and correlate logs from multiple sources, reducing noise from disparate systems. 2. FINE-TUNE DETECTION RULES Default detection rules often generate excessive false positives. Analysts can avoid unnecessary alerts by fine-tuning detection mechanisms to: • Set specific thresholds based on the environment and use case. • Reduce false positives by excluding benign behaviour patterns. • Update rules regularly to reflect evolving threats. Tip: Regularly review and customise detection rules in your SIEM or EDR tool based on your organisation’s risk profile. 3. GROUP ALERTS INTELLIGENTLY Alert storms often occur when multiple alerts are triggered for a single incident. Intelligent grouping helps analysts focus on the bigger picture by: • Aggregating alerts related to the same event or threat. • Using correlation rules to identify connections between logs and alerts. • Reducing the number of tickets created for similar incidents. Example: Implement alert deduplication and correlation logic in your SOC tools to group login attempts from the same source IP into a single incident. 4. PRACTICE GOOD ALERT HYGIENE Poorly managed alerts can clog the system, overwhelming analysts. Practising alert hygiene ensures that: • Old, irrelevant or low-priority alerts are reviewed and resolved promptly. • Alerts with no actionable outcomes are tuned or suppressed. • Historical alert data is archived but accessible for compliance and review. Tip: Conduct regular alert reviews to identify noisy rules and disable alerts that do not add value. 5. AUTOMATE REPETITIVE TASKS Manual alert triaging during a storm is time-consuming and error-prone. Automation can help SOC teams handle large volumes efficiently by: • Automating triage processes for known low-risk events. • Using SOAR tools to investigate and respond to alerts without human intervention. • Deploying playbooks for common incidents to reduce response time. Example: Configure your SOAR tool to automatically resolve low-risk phishing alerts by blocking the sender and tagging the email for further review. For more details, please refer to the attached PDF.

Alert fatigue undermines SOC effectiveness by overwhelming analysts with noise. To reduce false positives and optimize detection coverage, implement a structured, metric-driven tuning cycle: 1. Unique Analytic Identification - Ensure every detection rule carries a globally unique identifier. Embed this ID and the analyst’s final disposition (True Positive / False Positive) in each alert record. 2. Weekly Accuracy Reporting - Retrieve all resolved alerts on a weekly cadence. - Group records by alert ID to determine total firings per analytic. - Within each group, calculate the ratio and count of true versus false positives. - Produce comparative charts (e.g., stacked bars) to highlight high-volume and low-accuracy alerts. 3. Impact-Driven Prioritization - High Volume + Low Accuracy Example: Alert C fires 125 times but yields only 20 true positives (84% FP rate). Action: Refine detection logic, introduce additional context enrichment (threat intelligence feeds, user-/asset-based whitelisting), or consider rule deactivation if not business-critical. - High Volume + High Accuracy Example: Alert A fires 200 times at 90% true-positive rate. Action: Investigate upstream preventive controls (network segmentation, endpoint hardening) to reduce true detections at the source. - Low Volume + High Accuracy Example: Alert D fires 10 times with 100% accuracy. Action: Validate that tuning has not inadvertently introduced false negatives; maintain existing configuration. 4. Supplementary Metrics for Continuous Improvement - Mean Time to Triage (MTTT): Monitor triage latency to identify process bottlenecks. - False Negative Identification: Correlate incident post-mortems with missing alerts to uncover blind spots. - Automation Potential: Leverage enrichment playbooks and SOAR workflows to auto-close low-risk false positives or accelerate context gathering. 5. Institutionalizing the Tuning Lifecycle - Weekly SOC Briefings: Present alert-accuracy dashboards and tuning progress to stakeholders. - Quarterly Reviews: Reassess critical use cases, adjust thresholds based on evolving threat patterns, and validate rule efficacy against recent adversary behaviors. - Tuning Standard Operating Procedure: Maintain a living document that captures best-practice tuning techniques (e.g., threshold calibration, enrichment integration, correlation rule templates). By embracing this structured tuning methodology, SOCs can systematically reduce false-positive noise, accelerate genuine incident identification, and allocate analyst capacity toward proactive threat hunting rather than reactive noise management.

Today, I have came across Honeywell’s Executive’s Playbook to Industrial Autonomy where one of the customer (Chevron) stories is explained regarding AI-Assisted Alarm Management & Industrial Autonomy for Refining Processes 👉 How AI is working in this solution 1- Alarm Pattern Recognition & Mining Historical Data - The system ingests large volumes of historical alarm logs, operator responses, and process conditions. - AI models analyze these data streams to detect patterns (e.g., which alarm sequences commonly appear before a trip, what successful operator actions restored normal operation). - Instead of static alarm rationalization rules, AI learns correlations dynamically. 2- Alarm Guidance Application - When a new alarm comes in, AI compares it to similar past events. - It provides contextual, guided operator actions (like "Check valve position X before adjusting setpoint Y"), reducing trial-and-error. - This is not just rule-based i.e. AI is continuously refining recommendations as more plant data is collected. 3- Operator Decision Support / Reduced Cognitive Load - In traditional DCS systems, operators face alarm floods during upsets. - AI filters, prioritizes, and recommends likely causes + best corrective actions, lowering human stress and mistake probability. 4- Knowledge Capture & Transfer - Many senior operators are retiring, and their implicit knowledge is lost. - AI effectively acts as a knowledge-retention system by learning from historical operator interventions and embedding this experience into the system. - New operators get AI-assisted "coaching" in real time. Integration with Experion DCS platform 5- AI will not be running standalone but it’s integrated into Honeywell’s Experion Operations Assistant / DCS layer, ensuring real-time recommendations are actionable, safe, and visible in the control room (human-machine collaboration). 👉 👉 What type of AI is used? 1- Used Machine Learning (ML) — Both (Supervised + Unsupervised) - Example Supervised ML: Past alarms + operator responses labeled as "successful" or "unsuccessful" → models learn which actions are effective. - Unsupervised ML: Clustering alarm floods, discovering hidden correlations between process variables and alarm events. 2- Natural Language Processing (NLP) - Likely used in Alarm Guidance to translate data-driven insights into operator-readable instructions. - Helps in knowledge capture from manuals, SOPs, and historical logs. 3- Reinforcement Learning (RL) - Possible use for adaptive recommendations: the system tests guidance quality based on operator acceptance and process outcomes → continuously improves. 4- Expert System + AI Fusion - Alarm management has traditionally relied on rule-based expert systems (as per ISA 18.2 / IEC 62682 / EEMUA 191). - Honeywell’s & Chevron effort is not just static rationalization but adaptive, experience-driven alarm management (human-machine collaborative approach) Refer below Traditional approach v/s AI-assisted approach

Best Practices for SCADA Alarm Configuration and Rationalization Part 2 of 5 Proper alarm configuration and rationalization are crucial in SCADA systems it’s the difference between seamless operation and chaos, Poorly configured alarms can lead to operator overload and missed critical events, compromising operational efficiency and safety. Here's how to transform your SCADA alarm system from a source of stress to a tool of precision. Alarm Configuration Best Practices: 1️⃣ Setting appropriate alarm limits:  Define reasonable limits based on process requirements and equipment specs. Overly tight limits generate excessive alarms, while loose limits may miss important events. Collaborate with process engineers and operations staff to determine optimal limits. 2️⃣ Alarm prioritization:  Prioritize alarms based on their criticality and potential impact. Implement different priority levels to help operators quickly identify and respond to critical alarms. High-priority alarms should indicate events that require immediate action to prevent equipment damage or safety incidents. It’s about keeping focus where it matters most. 3️⃣ Alarm descriptions and annotations:  Ensure clear, concise alarm descriptions for operator understanding. Annotations or help text provide additional context, troubleshooting guidance, and recommended actions. No jargon, no confusion. Alarm Rationalization Process: 🔍 Regular review and analysis:  Regularly review historical alarm data, involving process experts, operations staff, and maintenance personnel. Analyze alarm frequencies, durations, and sequences to identify opportunities for improvement. Look for patterns, assess impact, and refine. 🚫 Identifying nuisance alarms and redundancies:  Eliminate nuisance alarms that provide little value, such as alarms on start-up/shutdown or known process upsets. Remove redundant alarms conveying the same information from multiple sources. ⚙️ Alarm suppression and filtering:  Suppress or filter alarms based on specific conditions or operational modes when appropriate. For example, suppress certain alarms during maintenance activities or filter alarms on equipment that is out of service. Keep your operators’ focus laser-sharp. Continuous Improvement: 📈 Monitoring and adjusting alarm configurations:  Continuously monitor alarm system performance and adjust configurations as processes, equipment, or operational requirements change. 👥 Operator feedback and training:  Solicit feedback from operators on the effectiveness of the alarm system. Provide training on alarm management best practices, including alarm response procedures and proper use of alarm systems. Foster a culture where feedback flows freely and training is continuous. Regularly evaluate and enhance your alarm management practices to unlock these benefits. What strategies have you implemented for effective SCADA alarm management?  Drop your experiences and tips in the comments below!

Alarm Flood - It's Our Own Darn Fault! A leader from the former Oil, Chemical and Atomic Workers Union once said, "The problem with you guys (management) is that you teach the employees how to work the machine, not how the machine works." What our union leader meant was that few workers understand the 'downstream implications of upstream actions.' That's how the machine works. Instead, many companies train their people how to manage the moment. That's working the machine. Imagine you're a refinery board operator. You are receiving five alarms EVERY minute. The frequency is too much to handle. What do you do? There's only one thing that you can do. "Reset and go!" This worker response is common in incident investigations. The Baker Report (2005) found that at BP Texas City, operators were "changing alarm set points without following required management of change procedures.” They were working the machine. To be clear, this is not a worker problem. It's a leadership, culture and technical issue. Last week, The Wall Street Journal noted that BP's Toledo refinery had 3,712 alarms over a 12-hour period. Why so many alarms? As they say, the road to hell is paved with good intentions. Designers of complex systems like to warn when stuff is out of design parameters. The unintentional consequence is too many alarms. The typical approach to reducing alarms is to launch a project. Projects include: 1. Identification of all alarms and confirm that they are relevant, unique, prioritized, understandable and timely.  2. Develop a process to manage and eliminate regular and nuisance alarms. 3. Have a process to authorize alarm overrides, with attention to safety critical systems and alarms. Managing the alarms alone will not prevent incidents. We must improve worker understanding of how the machine works. This means developing competency standards, training to standard, evaluating employee capabilities and using metrics to track results. To make this happen requires leadership, attention and focus. Some points to consider: - When employees reset alarms, do they understand the downstream implications? - Does the organization test for alarm management competency? - Are there metrics for worker competency? - Before changing an alarm point, is proper MoC done? - Do leaders engage workers about alarm floods and worker competency? - Is alarm flood management part of how process safety culture is defined? SafetyAnd offers unmatched safety expertise. We are collaborative and engaging consultants that achieve sustainable risk and safety improvements in record time! SafetyAnd offers safety and process safety program creation, C-Suite workshops, front-line supervisor training and the Safety Professional's Academy. #Safety #Leadership #ProcessSafety #AlarmManagement SafetyAnd Consulting Associates, Inc.