Abnormal Security vs Datadog

Discovers shadow AI tool/agent/chat usage via email, OAuth, identity and browser signals and enforces policy automatically.

Answers security teams' questions about threats, attack patterns and risk across email and SaaS on demand, without writing queries.

Phishing simulation and training that generates its exercises from the real attacks aimed at that organisation rather than from static templates.

Automates phishing triage: answers every employee report in seconds and remediates campaign-wide threats without an analyst in the loop.

Combines a custom outbound DLP policy engine with an AI triage agent that evaluates matches in context, releasing benign messages and quarantining likely violations.

Behavioral AI that automatically moves low-priority promotional email (graymail) out of the inbox without manual rules.

Behavioral AI that catches hijacked sessions, abused OAuth grants and privilege misuse across email, IdP and SaaS.

Behavioral-AI email threat detection that builds per-employee/vendor baselines to catch BEC, phishing and payload-free social engineering before delivery.

Analyzes identity signals pre-provisioning (HR systems, IdP, email) to detect synthetic personas and coordinated nation-state infiltration campaigns.

Inspects Microsoft Teams and Slack for malicious URLs and weaponized attachments, auto-remediating high-risk messages.

Uses behavioral AI to detect outbound email sent to unintended recipients and quarantines it before delivery.

Checks a Microsoft 365 environment against CIS Benchmarks, detects configuration drift in real time and provides prioritized remediation.

Detects and autonomously remediates compromised Microsoft 365/Google Workspace accounts by learning normal sign-in, device and behavioral patterns.

Detects AI-assisted pull requests from coding assistants such as Claude Code, Cursor and GitHub Copilot and compares them on adoption, PR throughput, cycle time, change failure rate and daily cost per active user.

Conversational AI interface for querying Datadog metrics, logs, traces and monitors in natural language and generating dashboards and notebooks, accessible from Datadog, Slack or mobile.

Datadog's Infrastructure-family product for shared GPU fleets across cloud, on-prem and neocloud providers: it links device health, cost and performance to the workloads and teams using them, alerts on unmet GPU requests, thermal throttling and ECC/XID errors, forecasts GPU demand and recommends optimisations such as reclaiming GPUs held by zombie processes. The page markets alerting, forecasting and recommendations but does not name a model or AI mechanism behind them.

Datadog's built-in AI engine: it continuously analyses metrics, traces and logs across the platform to raise anomaly alerts without configuration, detect faulty deployments by comparing code versions, run automated root-cause analysis on critical failures and surface tag-based insights and impact analysis. Available inside Infrastructure Monitoring, APM, Log Management and RUM rather than sold on its own.

Coding agent that triages production errors, regressions and vulnerabilities from Datadog telemetry, generates fixes with unit tests grounded in logs, traces and runtime variables, and opens pull requests for review.

AI SRE agent that autonomously investigates every alert the moment it fires, explores multiple root-cause hypotheses in parallel and reports findings into Slack, Jira, ServiceNow or GitHub.

Always-on AI SOC analyst that autonomously triages and investigates security alerts and delivers written investigation results to Datadog, Slack or Jira within minutes.