Sentinel AIAWS. Azure. GCP.One Platform.
Sentinel AI is an autonomous multi-cloud security platform that uses AI to detect, predict, and neutralise threats across AWS, Azure, and GCP — before they become incidents.
AI that works while your team sleeps
Sentinel AI combines graph-based threat modelling, reinforcement learning remediation, and explainable AI so every action is transparent and auditable.
AI Threat Detection
Temporal Graph Neural Network (T-GNN) models analyse cloud event sequences to detect anomalous behaviour invisible to rule-based systems.
Autonomous Remediation
Reinforcement learning (PPO) selects and executes the optimal remediation action — isolate, revoke, quarantine — with a full explainability trace.
Agentless Multi-Cloud
Native coverage across AWS, Azure, and GCP with no agents to deploy. Connects via cloud APIs and event streams — up and running in minutes.
Attack Graph Visualisation
Graph database (Neo4j) maps lateral movement paths and blast radius for every finding, so analysts understand the full attack chain at a glance.
MITRE ATT&CK Mapping
Every finding is automatically tagged to the MITRE ATT&CK framework technique, enabling structured threat intelligence and reporting.
SentinelGPT Copilot
LLM-powered security copilot explains findings in plain language, suggests remediation steps, and answers questions about your security posture.
Real-Time Findings Dashboard
Live console with WebSocket-powered alerts, posture score trends, and finding timelines across all connected cloud accounts.
Go CLI for DevSecOps
Native Go CLI for pipeline integration — run posture checks, pull findings, and trigger remediations from CI/CD without leaving the terminal.
From blind spots to autonomous defence
Sentinel AI is designed to be operational within hours, not months — with meaningful detection from day one.
Connect Your Clouds
Authorise AWS, Azure, and GCP accounts in minutes. Sentinel AI ingests CloudTrail, Audit Logs, and activity streams immediately — no agents, no code changes.
AI Builds Your Threat Model
The T-GNN model learns your environment's baseline over hours, not weeks. The graph engine maps identity, resource, and network relationships across accounts.
Detect, Explain & Alert
Anomalies surface as findings with MITRE ATT&CK tags, risk scores, attack graph paths, and SentinelGPT plain-language explanations — no analyst guesswork.
Remediate Autonomously or Manually
Approve autonomous remediation actions or execute them manually via the console or CLI. Every action is logged with before/after state for compliance.
One platform. Three clouds.
Sentinel AI provides native, agentless coverage across all three major cloud platforms with consistent detection logic.
- CloudTrail
- GuardDuty integration
- IAM analysis
- S3 exposure
- EC2 / Lambda
- Audit Logs
- Entra ID
- Resource Manager
- Storage accounts
- AKS workloads
- Cloud Audit Logs
- IAM analysis
- GCS buckets
- Compute Engine
- GKE workloads
Stop reacting. Start predicting.
Traditional SIEM and alerting tools generate noise. Sentinel AI uses graph-based context and AI reasoning to surface only the threats that matter — with the evidence and explanation to act immediately.
- Threat detection in hours, not days
- MITRE ATT&CK-mapped findings out of the box
- Autonomous remediation with full audit trail
- SentinelGPT explains every finding in plain language
- Built for multi-account, multi-cloud environments
Frequently Asked Questions
Everything you need to know about Sentinel AI.
Which cloud platforms does Sentinel AI support?
Sentinel AI monitors AWS, Azure, and GCP natively — no agents required. It connects via cloud APIs and event streams for agentless, API-native threat detection across all three major cloud providers.
How does Sentinel AI differ from a traditional SIEM?
Traditional SIEMs require manual rule creation, heavy tuning, and analyst time to investigate alerts. Sentinel AI uses machine learning to detect anomalies and MITRE ATT&CK-mapped threats automatically, with explainable findings and autonomous remediation — dramatically reducing mean time to respond.
What is autonomous remediation?
When Sentinel AI detects a confirmed threat — such as an exposed S3 bucket, a compromised IAM credential, or lateral movement — it can automatically take remediation actions (quarantine, revoke, isolate) within pre-defined policy boundaries. You maintain control of the blast radius; Sentinel AI handles the speed.
How long does Sentinel AI take to deploy?
Most organisations complete their initial connection and baseline within one business day. Sentinel AI is fully agentless — connect your AWS, Azure, or GCP accounts via API, and the platform begins monitoring immediately. No code changes or agent deployments are required.
Is Sentinel AI suitable for multi-cloud environments?
Yes. Sentinel AI is built for multi-cloud. It aggregates signals from AWS, Azure, and GCP into a single threat intelligence plane — enabling cross-cloud lateral movement detection that single-cloud tools cannot provide.
Ready to automate compliance or secure your cloud?
Request a personalised demo of AISEC or Sentinel AI — we will walk you through the platform with your own cloud environment in under 30 minutes.