Canonical solution label for systems centered on SOC workflows, enrichment, alert correlation, SOAR decisioning, and analyst-assist operations rather than a single low-level model family.
This application area focuses on transforming how IT operations teams monitor, detect, and resolve incidents across complex, hybrid and multi‑cloud infrastructures. Instead of relying on manual log review, static thresholds, and reactive firefighting, these systems automatically ingest and correlate data from monitoring tools, logs, metrics, events, and IT service management platforms to identify issues early, cut alert noise, and pinpoint root causes. By applying pattern recognition and predictive analytics, the tools surface the most important incidents, predict emerging failures, and trigger or recommend remediation actions. This reduces downtime, shortens mean time to detect (MTTD) and mean time to resolve (MTTR), and allows smaller teams to manage larger, more complex environments with greater reliability and better digital user experience.
Security Operations Automation focuses on using advanced software agents to streamline and partially or fully automate the work traditionally performed in a Security Operations Center (SOC) and network security teams. It covers activities like alert triage, incident investigation, threat hunting, playbook execution, change implementation, and incident documentation—tasks that are often repetitive, time‑sensitive, and spread across many tools. By turning natural‑language intentions (“investigate this alert”, “block this IP across edge firewalls”, “summarize this incident for compliance”) into consistent, auditable actions, this application area seeks to make security operations faster, more accurate, and less dependent on scarce expert labor. This matters because modern environments generate far more security telemetry and alerts than human analysts can realistically handle, while attackers increasingly use automation and AI to increase the speed and sophistication of their campaigns. Security Operations Automation uses large language models, reasoning agents, and orchestration platforms to correlate signals, recommend or execute responses, enrich investigations, and maintain human oversight for high‑impact decisions. The result is lower mean time to detect and respond, reduced analyst burnout, and a SOC that can keep pace with AI‑enabled threats and expanding attack surfaces.
This application area focuses on continuously identifying, prioritizing, and responding to cyber threats across endpoints, networks, cloud environments, and user accounts. It replaces or augments traditional rule‑based security tools and manual analyst work with systems that can sift through massive volumes of security logs, behavioral signals, and telemetry to surface genuine attacks in real time. The goal is to shrink attacker dwell time, catch novel and zero‑day threats that don’t match known signatures, and coordinate faster, more consistent incident response. It matters because the speed, scale, and sophistication of modern cyberattacks—often enhanced by attackers’ own use of automation and AI—have outpaced human-only security operations. By embedding advanced analytics into security monitoring, organizations can detect subtle anomalies, reduce alert fatigue, and automate playbooks for containment and remediation. This is increasingly critical for enterprises, cloud-centric organizations, and small businesses alike, all facing a widening cybersecurity talent gap and escalating regulatory and reputational risk from breaches.
This AI solution uses machine learning and generative AI to detect anomalous behavior across networks, endpoints, cloud workloads, and DevOps environments in real time. By automating intrusion detection, malware analysis, SOC workflows, and cyber threat intelligence, it accelerates threat response, reduces breach risk, and lowers the operational cost of security at scale.
Control room operators must make fast, high-stakes decisions on a rapidly changing electric grid while following procedures, cybersecurity constraints, and regulatory requirements.
Assesss and mitigates security risks in LLM applications across development and runtime, aligning controls to known LLM vulnerability categories and protecting deployed apps from emerging threats.
Enriches security alerts with attack context and analysis to help incident responders triage, investigate, and respond faster.
Uses coordinated AI agents to review high-volume security alerts, gather supporting evidence, and standardize on-call triage decisions for security teams handling billions of events per day.
This application area focuses on using advanced analytics to automatically detect, prioritize, and respond to cyber threats across an organization’s digital infrastructure. Instead of relying solely on static rules and manual review, systems continuously analyze network traffic, endpoint behavior, user activity, and system logs to spot anomalies, suspicious patterns, and emerging attack techniques in real time. The goal is to surface genuine threats quickly while suppressing noise, so security teams can act before attackers cause material damage or data loss. It matters because modern environments generate massive volumes of security telemetry that human analysts and legacy tools cannot keep up with. Attackers are faster, more automated, and more sophisticated, often blending in with normal activity to evade traditional controls. Intelligent threat detection helps organizations strengthen their defense posture, reduce alert fatigue, and dramatically shorten detection and response times, which is critical for protecting sensitive data, maintaining regulatory compliance, and ensuring operational continuity in both public and private sectors.
It addresses the problem of power grid congestion due to the increasing use of renewable energy sources, which can lead to inefficiencies and higher operational costs. Manual inspection in radioactive zones is slow, risky, and prone to human error. Grid operators need better ways to handle transmission congestion, which can threaten reliability and reduce operational efficiency.
This application area focuses on detecting and preventing fraudulent activity across telecommunications networks, services, and billing systems. It covers threats such as SIM swap and subscription fraud, account takeover, international revenue share fraud, roaming abuse, premium-rate scams, spoofed calls, and SMS phishing. The goal is to monitor massive volumes of call detail records, signaling events, billing data, device activity, and customer behavior in (near) real time to spot anomalies and suspicious patterns before losses accumulate. AI enhances traditional rules-based fraud management by learning normal behavior, adapting to evolving attack vectors, and prioritizing the riskiest events for action. Techniques like anomaly detection, graph analysis, and sequence modeling help identify subtle, cross-channel fraud schemes that static rules miss, while generative and analytical tools assist investigators with faster triage and explanation. This reduces revenue leakage, limits customer churn, and helps operators and partners meet regulatory and national-security expectations for securing communications infrastructure.
Trains custom attribution models from campaign conversion paths and syncs offline conversion outcomes to ad platforms like Google and LinkedIn to improve channel credit assignment and campaign optimization.
Monitors remote mining site perimeters to detect unauthorized access, theft, and vandalism, supporting incident verification despite limited network connectivity.
Detects anomalous alarms and operational events across telecom networks to prioritize likely faults, accelerate service assurance, and reduce operations and maintenance effort.
Detects anomalous behavior across cloud accounts and services from cold start, reduces non-actionable alert noise for on-call teams, and supports service mapping and proactive incident response in complex IT environments.
Combines in-cab video evidence workflows and ELD data analytics to defend against fraudulent claims, improve driver coaching and fleet efficiency, and strengthen negotiations with insurers and shippers.
Automates security response workflows by using Splunk threat intelligence in SOAR playbooks to enrich detections and trigger consistent, rapid downstream mitigation actions.
Supports planning of covert data exfiltration by minimizing transfer volume and adapting theft methods to network and security constraints.
Detects cybersecurity anomalies and applies trusted, context-aware automated remediation to scale incident response and shift from reactive handling to proactive prevention.
Monitors telecom signaling traffic to detect SS7 and SIP attacks that evade traditional billing-record-based security monitoring.
AI-assisted secure remote access for hybrid workforce connectivity, replacing legacy VPN and proxy approaches with improved visibility, policy control, and protection for user and application traffic across cloud and hybrid environments.
Provides secure AI agent access to cloud operational data for DevOps workflows while sanitizing and defending telemetry inputs to protect LLM-driven AIOps pipelines from manipulation within existing cloud trust boundaries.
Converts threat intelligence between MISP and STIX formats so security teams and platforms can share indicators and context across different ecosystems with less manual reformatting.
Generates SOC attack narratives by correlating raw logs and disconnected vendor alerts into related alert summaries, helping analysts understand attack scope, sequence, risk, and whether alerts represent real threats or noise.
Continuously evaluates AI-assisted access decisions for traceability, performance, and regression detection, while generating security incident reports from multi-source investigation data to support knowledge sharing, post-incident reviews, and compliance reporting.
Automates repetitive SOC response workflows while keeping analysts in control, coordinating actions across Security, IT, and infrastructure teams, and using LLM-assisted planning to speed incident triage, threat analysis, and mitigation steps such as DDoS response.
AI-assisted summarization of log-triggered security or operational alerts to distill thousands of log lines into concise incident context and likely root cause hypotheses, reducing manual investigation time and analyst burden.