AI Security and Anomaly Detection is a market focused on providing runtime protection and monitoring for AI applications, particularly those using generative models like large language models (LLMs). These solutions detect and mitigate risks such as prompt injection, hallucinations, toxicity, biased outputs, data leakage, and performance drift. Delivered as cloud-native modules via APIs or embedded within applications, they offer real-time visibility into content and security anomalies. The market supports compliance with emerging regulations, enables centralized oversight across multiple AI deployments, and helps organizations safeguard their brand and decision-making processes from faulty or malicious AI behavior.
Infrastructure monitoring tools capture the health and resource utilization of IT infrastructure components wherever they reside (e.g., in a data center, at the edge, or IaaS or PaaS in the cloud). This enables I&O teams to monitor the availability and resource utilization data of physical, virtual, software entities, and AI systems — including servers, containers, network devices, database instances, hypervisors, storage, and basic application monitoring. These tools collect data in near real time and perform historical data analysis or trending of the elements they monitor.
Gartner defines observability platforms as products that help organizations understand and optimize the health, performance and behavior of applications, services, infrastructure and AI agents, as well as user experience. They ingest and analyze telemetry such as logs, metrics, events and traces to detect issues that affect end users, enabling early remediation. These platforms are used by IT operations, SRE, platform engineering, developers, security teams and product owners. Modern businesses rely on critical digital applications and services that directly influence revenue, client satisfaction and brand reputation. Outages, latency and degraded performance harm these outcomes. Observability platforms address this by ingesting, correlating and analyzing telemetry from applications, infrastructure and AI systems to detect anomalies, identify the root cause of issues and quantify user‑experience impact. These capabilities enable organizations to improve the availability, performance and resilience of digital services. As a result, observability platforms support revenue‑loss avoidance through early detection, faster mean time to resolution and prevention of customer‑impacting failures. They also accelerate development and platform engineering workflows by providing continuous feedback on code changes, deployments and model behavior. This allows teams to deliver new features and AI capabilities faster while maintaining reliability and improving customer experience.
Security information and event management (SIEM) is a configurable system of record that collects, aggregates and analyzes security event data from on-premises and cloud environments. SIEM processes security event data for the purposes of threat detection, investigation and response. It natively supports data normalization and offers user-configurable detection content and reporting to orchestrate threat mitigation and satisfy compliance requirements. These solutions are delivered via a SaaS platform or client-hosted on-premises or private cloud. The security information and event management (SIEM) system must assist with: 1. Aggregating and normalizing data from various IT and operational technology (OT) environments. 2. Designing and executing near real-time monitoring and alerting content. 3. Enriching and investigating security events of interest. 4. Supporting manual and automated response actions. 5. Maintaining and reporting on current and historical event data.