Agentic AI
Data Scientist
The only autonomous data science agent purpose-built for ICS/OT security. State a goal in natural language. Receive a cryptographically sealed, evidence-validated dataset. Human approval required before any engine runs.
Six Stages from Goal to Sealed Dataset
Human approval required before any engine runs. Every step is anchored in a tamper-evident audit trail.
State a goal in natural language
Describe what you need in plain English. "Generate 200K labelled ICS attack dataset for IDS training" is enough. The agent understands ICS/OT context, MITRE ATT&CK for ICS taxonomies, and industrial protocol semantics.
Agent planner creates a typed multi-step plan
An ICS/OT-aware planner decomposes your goal into a sequence of typed pipeline steps. Each step specifies the target engine, input parameters, quality targets, and estimated credit cost.
Human Approval Gate
You review every generated plan before any engine is invoked. Inspect step parameters, estimated credit cost, engine selection, and quality targets. Nothing runs without your explicit sign-off.
Plan executor runs each step against the capability stack
The executor invokes each step sequentially against the full SynthLab capability stack: Mock Data, Synthesize, Constrained Synthesis, Virtual SCADA Simulator, and ICS Security Simulator.
Agent reads the signed evidence bundle
After every step, the agent programmatically reads the signed evidence bundle and verifies the cryptographic seal — covering job spec, run log, quality reports, and constraint satisfaction.
Self-healing execution when results fall short
If realism score, constraint satisfaction, or privacy metrics fall below the plan threshold, the agent proposes a targeted patch plan grounded in cryptographically-verified facts — not guesses. Every decision is anchored in a tamper-evident audit trail.
Built for Autonomous, Auditable Data Science
Six capabilities that separate the Agentic Data Scientist from generic AI wrappers.
Evidence Self-Healing
Reads the complete signed evidence bundle after every pipeline step. When realism, privacy, or constraint metrics fall below threshold, the agent identifies exactly which check failed and proposes a grounded patch.
Human Approval Gate
Every generated plan requires your explicit sign-off before any engine is invoked. Review step parameters, credit estimates, engine selection, and quality thresholds. Modify, reject, or approve.
Auditable AI Decision Trail
Every autonomous decision the agent makes — plan creation, step execution, evidence reading, patch proposal — is anchored in a tamper-evident audit trail. Compliance teams can trace every reasoning step.
Multi-Engine Orchestration
The agent can invoke any SynthLab capability in sequence: Mock Data, Synthesize, Constrained Synthesis, Virtual SCADA Simulator, and ICS Security Simulator. A single plan can chain multiple capabilities for complex datasets.
ICS/OT Domain Intelligence
ICS/OT-aware planning with MITRE ATT&CK for ICS taxonomies, industrial protocol semantics (Modbus TCP, OPC-UA, BACnet/IP, DNP3, MQTT, IEC 61850), and physics-aware behaviour for realistic OT data generation.
Credit-Controlled Execution
Every plan displays an estimated credit cost before execution. The agent will not exceed approved credit limits. Execution stops and awaits human review if cost projections change during a run.
Cryptographic Evidence, Not Confidence Scores
| Agentic Data Scientist | Generic AI Tools | |
|---|---|---|
| Validation source | Signed evidence bundle | Confidence scores and loss metrics |
| Self-healing basis | Cryptographically-verified failures | Statistical heuristics and guesses |
| Decision auditability | Tamper-evident decision trail | Black-box model decisions |
| Human oversight | Required gate before any engine runs | Optional, often absent |
Deploy an autonomous data science agent that reads evidence, not confidence scores
The Agentic Data Scientist ships as part of SynthLabTech. Visit the product site to run it against your own data.