🔁 Build Once. Learn Always. Inside the Autonomous Detection & Response Loop

John W8MEJ Menerick · April 4, 2025

Security operations don’t just need to scale. They need to evolve. And that means replacing brittle rules with systems that learn, respond, and improve—on their own.

Let’s be honest—static playbooks aren’t enough anymore. You can’t write a workflow for every edge case. Threats change. Your infrastructure changes. And every incident teaches you something that gets lost in the backlog.

But what if your detection and response system actually learned from every incident?

This is the power of a closed-loop architecture for autonomous SecOps—one that turns your operations pipeline into a self-improving engine.

🧠 The Mental Model: From Pipeline to Feedback Loop

Most SOC pipelines today look like this:

Log ingestion → Detection → Alert → Analyst Review → Playbook → Resolution

Linear. Manual. Fragile.

Here’s the upgraded loop:

Engineer Self-Service → ETL + Schema Inference → EBM Detection → SOAR Playbook Mgt. → Result Logging → Simulation & Testing → Optimization Engine → Back to Detection

It’s not just automated—it’s reflexive. Every action leads to new data. Every mistake leads to a model or playbook improvement. The system doesn’t just run—it learns.

🛠️ What’s Inside the Loop?

✅ Step 1: Engineer Self-Service Onboarding

When a new service or app is launched, the engineer registers their log source via a lightweight UI or API. No need for predefined schemas. The logs are routed into the system and staged for analysis.

🔍 If the engineer doesn’t know the schema?
No problem. The ETL pipeline automatically infers structure and extracts dimensional vectors.

✅ Step 2: ETL & Feature Engineering

Google Colab or other ETL backends clean, transform, and enrich the logs dynamically—turning raw text into structured event vectors.

✅ Step 3: EBM Scoring

An energy-based model analyzes every event and assigns an anomaly score. Events with high “energy” deviate from learned normal behavior.

✅ Step 4: SOAR Playbook Creation & Execution

For high-risk events, the system auto-triggers a playbook creation and / or optimization that performs containment, enrichment, escalation, eradication—or all four.

✅ Step 5: Simulation & Feedback

Synthetic threats are injected to validate the entire pipeline. Did the right detection trigger? Did the playbook behave as expected?

✅ Step 6: Optimization Engine

A reinforcement learning (RL) agent or genetic algorithm proposes improvements to detections and playbooks based on failure cases or drift.

Contribution	Innovation
Self-Service Log Onboarding	Reduces security friction in DevOps pipelines
Schema Inference in ETL	Enables true zero-touch log ingestion
Energy-Based Model Scoring	Improves anomaly detection with better uncertainty modeling
Closed-Loop Playbook Creation & Optimization	Adapts responses based on performance, not just static logic
CI/CD for Playbooks	Treats security automation as code—tested, versioned, deployed automatically

or another workflow suiting ITIL based institutions may look like the following;

If you didn’t want the self-service workflow

For instance, you operate within a COBIT environment

🔍 Real Example: Playbooks That Improve Themselves

In a recent environment, simulated insider threat behaviors were introduced into the pipeline weekly. The optimization layer:

Flagged playbooks that missed high-energy detections
Proposed logical changes (e.g., new conditions, reduced timeouts)
Validated changes through simulation
Automatically promoted successful improvements

The result? Mean time to response dropped 57%—without a single new rule manually written.

🤖 Why Static Systems Break Under Pressure

Traditional SOC architectures break because:

Detection rules go stale
Playbooks grow unmanageable
Incident learnings get lost in Slack threads

An autonomous loop solves this by:

Learning from what’s normal—and what’s not
Continiously refining playbooks
Using feedback from real incidents and tests to evolve

🎯 Your Move

If you’re still maintaining brittle playbooks by hand, ask yourself:

What if the system could evolve them for you—and every new service onboarded itself?

👉 Get in the loop. Automation is step one. Autonomy is the future. Read the full white paper or dive into the latest podcast episode to learn more.

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