Diving Deeper into AI implementation with Splunk
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Key Takeaways
⇨ Artificial Intelligence has emerged as the largest market disruptor in business technology today, as organizations around the world scramble to integrate AI capabilities into their IT infrastructure.
⇨ In cybersecurity, Splunk employs AI to detect anomalies, provide risk-based alerting, and identify domain generation algorithms (DGAs) automatically.
⇨ Real-world applications of Splunk’s AI technologies include a leading European luxury car manufacturer using Data Science and Deep Learning (DSDL) to predict and prevent production errors, thereby enhancing process efficiency and quality.
Artificial Intelligence has emerged as the largest market disruptor in business technology today, as organizations around the world scramble to integrate AI capabilities into their IT infrastructure. While many organizations are quick to invest into AI, some are still hesitant to implement AI technology within their business systems citing fears over data security and compliance, according to research conducted by SAPinsider on AI and Automation.
Splunks’ AI innovations can best be described in five broad categories –
Embedded Product Capabilities
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Splunk is actively integrating AI across its platform to enhance user experiences and outcomes. AI, as defined by Splunk is the ability of computer software to simulate human reasoning, embedded deeply within their products, spanning cybersecurity, IT operations, and application development.
Customized AI and ML
In cybersecurity, Splunk employs AI to detect anomalies, provide risk-based alerting, and identify domain generation algorithms (DGAs) automatically. For IT operations, their AI capabilities include adaptive thresholding, alert storm detection, and incident similarity suggestions. In application development, Splunk uses AI for predictive alerting and baseline deviation detection.
Splunk offers tools like the Splunk Machine Learning Toolkit (MLTK) and Splunk Data Science and Deep Learning app (DSDL) to facilitate machine learning and deep learning tasks. MLTK simplifies common ML operations, while DSDL enables training and inference of deep learning models using tools like Jupyter Notebook and TensorFlow.
Extensible AI libraries and APIs
The platform’s extensible nature allows customers to integrate their own models, leveraging APIs and libraries for flexibility. Notably, Splunk supports externally trained ONNX models within MLTK, accommodating diverse data science workflows and preferences.
Generative AI
Looking forward, Splunk is investing in generative AI (GenAI) capabilities, as shown by the Splunk AI Assistant. This tool enhances user proficiency with Splunk’s query language (SPL) through natural language prompts, simplifying data analysis and platform navigation.
Guided Assistive Workflows
Splunk’s upcoming Security and Observability assistants use GenAI to streamline data analysis, guiding users through incidents with relevant suggestions and transforming complex data into actionable insights. More details were announced at the Splunk .conf24 conference in June, featuring a keynote highlighting the use of Splunk AI capabilities to optimize a global medical device manufacturer’s supply chain.
Real-world applications of Splunk’s AI technologies include a leading European luxury car manufacturer using DSDL to predict and prevent production errors, thereby enhancing process efficiency and quality.
Splunk’s approach to AI encompasses embedded AI functionalities, extensible libraries, and cutting-edge generative AI solutions, aimed at empowering users across various industries with advanced data analytics capabilities.
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