Splunk Blogs

Learn how digital steganography allows attackers to hide malicious data in plain sight. Discover how this technique bypasses security and how to detect it using behavioral analysis.

F1 score provides a more reliable performance metric than accuracy for imbalanced AI tasks, such as toxicity detection and safety classification.

Hallucinations in autonomous agents create business liability. Learn how teams can verify model outputs against certified source data, in a systematic way.

Measure LLM reliability and performance using seven key metrics to isolate system health from generation quality.

This field guide categorizes AI agents across seven levels of autonomy, providing a roadmap for mapping system architectures against common failure modes and production-ready requirements.

Measure and improve AI accuracy by distinguishing between classification metrics, generative faithfulness, and agentic trajectory completion.

Learn how to validate AI models across the full lifecycle. From development evals to runtime guardrails, close the gap between benchmarks and production behavior.

Enterprise AI requires core components—including data pipelines, security, and observability—to successfully move agentic applications from demo to production.

Understand context engineering as a systematic discipline for curating agent inputs to improve reliability, reduce costs, and optimize performance.