Darktrace
Integrations
- AWS
- Microsoft Azure
- CrowdStrike
- Splunk
- ServiceNow
- Okta
- Palo Alto Networks
Pricing Details
- Costs are determined by entity volume, network throughput, and module selection (PREVENT/HEAL).
- TCO analysis typically requires a partner-led assessment.
Features
- Closed-Loop AI Orchestration
- Multi-Tiered Governance Modes
- Approximated Policy Restoration (HEAL)
- Proprietary Time-series Metadata Store
- Immutable Audit Logging
Description
Darktrace 2026: Closed-Loop Cyber AI Architecture Review
The Darktrace architecture in 2026 is centered on the 'Cyber AI Loop,' a continuous feedback mechanism linking vulnerability prioritization (PREVENT) with active defense (RESPOND) and recovery (HEAL) 📑. Data integrity is maintained via a Proprietary Time-series Behavioral Metadata Store, which logs entity interactions to inform Bayesian risk calculations 📑.
Autonomous Response & Governance Modes
The Antigena module executes containment through targeted session termination and access control modifications. To address the risk of Denial of Service (DoS) from automated actions, the system enforces granular governance tiers 📑.
- Operational Modes: Supports 'Passive Monitoring,' 'Human-in-the-Loop' (confirmation required), and 'Automated-with-Guardrails' for specific low-risk network segments 📑.
- Response Precision: Uses recursive Bayesian estimation to ensure actions are proportionate to the calculated threat probability, reducing the likelihood of business disruption 🧠.
⠠⠉⠗⠑⠁⠞⠑⠙⠀⠃⠽⠀⠠⠁⠊⠞⠕⠉⠕⠗⠑⠲⠉⠕⠍
Resilience & Recovery Mechanisms (HEAL)
Darktrace HEAL focuses on approximated policy restoration by identifying historical configurations that align with current security requirements 📑. This process is not a raw 'system restore' but a policy-driven approximation of a neutralized state.
- Immutable Audit Trail: All autonomous recovery actions are logged within an immutable ledger to ensure compliance and manual rollback capability 🧠.
- KPI Tracking: Platform metrics indicate an average reduction in MTTR (Mean Time to Repair) of up to 70% in documented enterprise deployments 📑.
Evaluation Guidance
Technical teams must conduct a performance validation plan prior to full deployment. 1. Throughput Testing: Require vendor p99 latency benchmarks for sensors on 100Gbps+ links to ensure zero packet loss 🌑. 2. Drift Analysis: Implement synthetic drift injection in auto-scaling cloud environments to measure retraining window efficiency 🌑. 3. Recovery Benchmarking: Execute a Rollback Playbook test to verify the integrity of HEAL-restored policies in a staged VLAN environment 🌑.
Release History
Full rollout of 'Self-Healing Networks'. Autonomous reconfiguration of network topology to isolate zero-day threats in real-time.
Introduction of Predictive Policing and Forecast modules. forecasting potential attack vectors in edge computing environments.
Acquisition by Thoma Bravo. Platform consolidation into the 'Cyber AI Loop'. Enhanced OT anomaly detection with physics-informed AI.
Introduction of Cyber AI Analyst and Darktrace HEAL. Automated incident investigation and recovery of affected systems.
Darktrace SaaS and Darktrace PREVENT launched, focusing on attack surface management and cloud-native security.
Enhanced AI models for supply chain attack detection. Improved user interface and reporting for executive compliance.
Introduction of Darktrace Industrial for OT environments. Launch of Antigena Email for AI-driven phishing and spoofing protection.
Expanded coverage to include cloud environments (AWS, Azure, GCP). Enhanced integration capabilities with SIEMs and SOAR tools.
Launched Antigena, the autonomous response system. Enabled automatic neutralization of threats like ransomware at machine speed.
Introduction of the Threat Visualizer, providing a 3D graphical representation of real-time threat activity. Improved algorithm accuracy.
Initial commercial release. Focused on anomaly-based network detection using unsupervised machine learning to establish a 'pattern of life'.
Tool Pros and Cons
Pros
- Real-time detection
- Autonomous response
- AI-powered learning
- Proactive resilience
- Reduced workload
Cons
- Potentially expensive
- Complex setup
- False positives
