How to Generate Synthetic AI Agent Training Data

Logs and telemetry are the foundation of every modern detection pipeline and every security-focused AI agent. But high-quality, labeled attack data is notoriously scarce. Real-world telemetry is overwhelmingly benign, malicious events are rare, and reconstructing—then labeling—a complete attack scenario is slow, costly, and operationally fragile. The result: detection engineering stalls, and AI agents get trained on sterile, theoretical datasets that crumble against real adversaries.

This guide breaks down the approaches available for generating synthetic cybersecurity training data, introduces a maturity framework for evaluating fidelity, and walks through SimSpace’s Gartner-validated methodology for producing hyper-synthetic data: full-stack, precision-labeled telemetry generated in real time from an enterprise replica.

Comparing Synthetic Data Generation Methods

The first three approaches each solve a slice of the problem. Static datasets give you reproducibility but no freshness. Rule-based and AI-assisted generation give you scale but inherit a fidelity ceiling—they approximate what real telemetry looks like rather than producing telemetry inside a real-behaving environment. Hyper-synthetic data closes that gap by generating labeled telemetry from a live replica of the production estate.

The Synthetic Data Maturity Framework

Not all synthetic data is created equal. Use this four-level framework to assess where any dataset—or any generation method—sits on the fidelity curve.

Level 1 — Static Datasets

Pre-captured artifacts: PCAP files, CTI feeds, and published corpora. They are easy to acquire and reproducible, which makes them useful for baseline benchmarking. But they are frozen snapshots of a single environment. They age fast, can’t represent your stack, and offer no path to generate new scenarios. Labeling is whatever the original authors provided.

Level 2 — Rule-Based Generation

Automated log creation driven by templates and scripted rules. This breaks the supply ceiling of static data and lets teams produce volume on demand. The weakness is rigidity: rule-based logs reflect the assumptions baked into their templates, not the emergent behavior of real tools interacting under load. Coverage of novel or multi-stage attacks is limited to what someone explicitly scripted.

Level 3 — AI-Assisted Generation

The pattern-based approach exemplified by Microsoft’s AI-assisted synthetic attack log work: translate attacker TTPs and actions into structured log entries using LLMs, agentic refinement loops, and reward-based tuning. This is a meaningful step up—it captures multi-event sequences, preserves parent-child process relationships, and produces realistic command lines without standing up a lab for every scenario.

Its acknowledged limitation is fidelity gap. As Microsoft’s own research notes, generated logs “still differ noticeably from real event logs, especially in process paths, command-line arguments, service names,” and closing that gap toward verbatim realism “will require a substantial amount of labeled data.” The model is reasoning about what telemetry should look like, not observing telemetry produced by a real environment.

Level 4 — Hyper-Synthetic Data

SimSpace’s approach. Instead of generating logs that imitate a real environment, you stand up a high-fidelity replica of the production enterprise—security stack, productivity applications, network architecture, and realistic user behavior—and run real attacks against it. The telemetry isn’t approximated; it’s emitted by real tools reacting to real adversary actions, captured and labeled as it happens.

This is the tier validated by Gartner® in its Emerging Tech report, Hyper-Synthetic Data Is Essential to Winning the Future of Cybersecurity. Hyper-synthetic data delivers full-stack, multi-vector, precision-labeled telemetry under authentic background noise—the conditions AI agents actually face in production.

Methodology: Generating Labeled Attack Data at Level 4

The SimSpace AI Proving Grounds is an isolated, realistic cyber simulation platform purpose-built to generate hyper-synthetic, production-grade log data. The workflow has three core stages.

1. Replicate the Production Environment

Build an isolated, enterprise-mirrored environment that reflects the real security stack and everyday productivity app traffic. This matters because telemetry fidelity is a function of environment fidelity. A credential-dumping event captured against a realistic Windows estate—with genuine background user activity, real EDR sensors, and real network services—carries the noise, timing, and contextual signal that a templated or LLM-generated log cannot reproduce. The replica establishes the realistic background conditions that separate a robust model from a brittle one.

2. Run Automated Attacks

Launch targeted adversary emulation—nation-state and e-crime campaigns—inside the isolated environment. Because the attacks execute against real tooling, they force genuine tool interactions and emit authentic telemetry across the full stack rather than a synthetic projection of expected behavior. Multi-stage attack chains play out end to end, producing the cross-host behaviors and event sequences that detection and AI models need to learn.

3. Capture Labeled Telemetry in Real Time

As attacks execute, comprehensive multi-vector log data is captured and exported—automatically labeled at the source. Because the platform knows exactly which actions are being executed and when, malicious activity is precision-labeled against benign background traffic in real time. No post-hoc reconstruction, no manual labeling backlog, no ambiguous ground truth. Data science and product teams receive clean, real-world log metrics ready to train agent model candidates.

This is the critical inversion versus Level 3 approaches: instead of generating data and then trying to verify how realistic it is, hyper-synthetic generation produces data that is real telemetry by construction, with labels guaranteed by the simulation’s own knowledge of the attack timeline.

From Data to Mythos-Ready Agents

The same environment that generates the data also validates the models trained on it. Newly minted agents go back into live-fire scenarios so teams can build and train red, blue, and control AI agents against realistic adversaries, then benchmark and quantify their step-by-step performance. The outcome is compressed time-to-market—eliminating the weeks normally lost hand-crafting and cleaning datasets—and AI validation grounded in proven operational metrics. 

Frequently Asked Questions

What is hyper-synthetic data in cybersecurity?

Hyper-synthetic data is full-stack, precision-labeled security telemetry generated in real time from a high-fidelity replica of a production enterprise environment. Rather than approximating what attack logs should look like—the pattern-based approach of AI-assisted generation—hyper-synthetic data is emitted by real security tools and applications reacting to real adversary emulation under authentic background noise. Gartner identifies it in its Emerging Tech report as essential to winning the future of cybersecurity, because it provides the production-grade, context-rich data that AI agents require to perform reliably against real-world attacks.

How do you generate labeled attack data for AI model training?

The high-fidelity method follows three stages. First, replicate the production environment—the security stack, productivity applications, and realistic user behavior—inside an isolated platform. Second, run automated adversary emulation, launching nation-state and e-crime campaigns that force genuine tool interactions. Third, capture the resulting multi-vector telemetry as the attacks execute, with malicious activity precision-labeled against benign traffic in real time because the platform knows exactly which actions ran and when. This eliminates manual labeling and the costly reconstruction of attack scenarios, yielding clean ground truth ready for model training.

What is the difference between synthetic data and production data for security models?

Production data is real telemetry from a live environment. It is authentic but overwhelmingly benign, with rare and sparsely labeled malicious events, and using it raises privacy and exposure concerns. Conventional synthetic data—rule-based templates or AI-assisted log generation—solves the scale and privacy problems but introduces a fidelity gap, diverging from real telemetry in process paths, command-line arguments, and service names. Hyper-synthetic data resolves the trade-off: it is generated inside a realistic enterprise replica, so it carries the fidelity and background noise of production data while delivering the scale, safety, reproducibility, and precise labeling that production data cannot.

To learn more about the impact of hyper-synthetic data on building and training AI agents, read the Gartner® report: Emerging Tech: Hyper-Synthetic Data Is Essential to Winning the Future of Cybersecurity.