Empower your observability strategy with AI that adapts to your infrastructure, simplifies operations, and enables faster, data-driven decisions.
Artificial Intelligence (AI) is transforming how organizations manage observability, data pipelines, and infrastructure at scale. Within Cribl’s suite of products, AI is applied to solve real-world operational challenges—automating data routing, accelerating root cause analysis, detecting anomalies, and optimizing system performance across complex environments. Cribl’s AI-driven capabilities are purpose-built for modern data stacks, helping teams reduce manual effort, improve visibility, and make faster, smarter decisions with their data.
You don’t often see real change, but As Dewey Wilkerson said, "The future is now, old man"—and today that means AI. Artificial Intelligence/Machine Learning tool sets like ChatGPT are offering broad capabilities that will enable and benefit all.
Your enterprise’s business goals include strong security, high-service stability, and increased customer experience. To understand how you’re meeting those goals, you must collect and analyze data correlated with your desired outcomes.
AI can assist by solving a multitude of problems in IT, ranging from automating routine tasks and enhancing cybersecurity to optimizing IT operations and providing valuable data-driven insights. AI excels at data analysis, predictive maintenance, and fraud detection, improving efficiency and reducing costs. Additionally, AI can assist in customer support, software development, and IT infrastructure management.
Sensitive Data Identification: AI can proactively identify and protect sensitive data within platforms, ensuring compliance with security standards and reducing the risk of data breaches.
Anomaly Detection and Monitoring: AI can continuously monitor system activity for anomalies, such as unusual patterns in cloud event logs or unauthorized file changes. This proactive detection helps identify potential security threats before they escalate.
SIEM Optimization: AI can help manage SIEM costs by analyzing and selectively dropping specific events to control events-per-second (EPS) growth. For example, Cribl Stream can filter out unnecessary events while maintaining compliance with expected data formats like LEEF.
Real-Time Pipeline Monitoring:AI can monitor the real-time status of data pipelines, including metrics like data throughput, error rates, and processing delays. This helps ensure smooth data flow and quick identification of bottlenecks or anomalies.
Performance Tuning and Resource Utilization Tracking: AI can analyze system resource utilization (CPU, memory, network bandwidth) to predict future needs and optimize pipeline configurations for better performance.
Infrastructure Monitoring: AI can enhance infrastructure monitoring by collecting and processing observability data in real time. For example, Cribl Edge can integrate with visualization tools like Grafana to display system metrics and identify issues faster.
Dynamic Resource Allocation in Kubernetes: AI can optimize resource allocation in Kubernetes environments by managing auto-scaling policies. It can predict traffic spikes and adjust pod scaling to handle fluctuations in data volume, minimizing disruptions and improving system performance.
Failure Planning and Risk Mitigation: AI can analyze failure scenarios and recommend contingency plans to balance cost versus risk. For example, it can help determine the level of resources needed to achieve desired reliability levels in system deployments.
Optimizing Distributed Deployments: AI can assist in architecting distributed deployments by analyzing data flow, resource utilization, and failure scenarios. For example, Cribl Stream uses distributed deployment architectures with multiple Worker Nodes to ensure scalability and reliability, reducing the risk of data loss during outages or maintenance.
Cribl AI helps organizations accelerate investigations, uncover risk, and move faster. By applying intelligence across collection, processing, management, and investigation workflows, Cribl AI transforms raw telemetry into operational value while building the AI-ready foundation your teams and AI agents need to succeed.
Artificial intelligence is a means to provide customer-focused solutions. It is the “how” behind what Cribl delivers, not the “what.” Cribl AI speeds time to value, uncovers risk, and enhances team productivity by applying intelligence to reversibility pipelines and across the data management lifecycle. From creating pipelines and queries using natural language, to automatically parsing and optimizing data, detecting sensitive data in motion, accelerating investigations, and supporting AI-ready telemetry strategies, Cribl AI helps teams accomplish more with less effort. These AI-powered capabilities enable IT and security teams to work faster, make better decisions, and get more value from their data.
Cribl AI helps teams work faster by automating and simplifying telemetry management tasks. Use natural language to create and modify pipelines, queries, and configurations, and vibe-code custom apps tailored to your unique workflows. Cribl Copilot provides real-time troubleshooting guidance, answers product and configuration questions, and helps teams resolve issues faster without requiring deep product expertise. Connect to external tools through Cribl MCP to extend AI-driven operations across your environment.
Accelerate investigations with agentic, question-first workflows that help teams quickly find answers, explore data, and uncover insights. Use plain English to build queries, investigate incidents, generate reports and visualizations, and enrich findings with human context and external sources. The result is faster investigations, less manual effort, and better decisions.
AI-powered background detection in Cribl Guard helps organizations uncover previously unknown sensitive data in motion before it creates privacy, security, or compliance risks. With Bring Your Own Model (BYOM), teams maintain full control by using Cribl AI with their approved AI backend, enabling AI adoption on their own terms.
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