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Intermediate20 min

Brain Command Center

Master the Evolutionary Brain — OODA loops, signal processing, evidence chains, and learning from outcomes

The Brain Command Center is the intelligence hub of BrainstormMSP. It houses the Evolutionary Brain — an AI reasoning system that observes signals, orients context, decides actions, and acts with evidence. The brain learns from every outcome to improve over time.

1

Evolutionary Brain

What is the Evolutionary Brain?

The Evolutionary Brain is BrainstormMSP's core AI reasoning engine. Unlike static rule systems, it:

**Learns from outcomes**: Every decision result feeds back into the brain

**Adapts confidence**: Confidence scores adjust based on success/failure history

**Evolves strategies**: Strategies that work well get reinforced; poor ones get deprioritized

The 5 Brain Tabs

1. **Overview** — High-level brain health, decision count, confidence distribution

2. **Decisions** — Timeline of all brain decisions with reasoning chains

3. **Remediation** — Auto-remediation actions taken and their outcomes

4. **Observatory** — Real-time signal and event stream

5. **Insights** — Learned patterns and trend analysis

2

OODA Loop

Observe → Orient → Decide → Act

Every 10 seconds, the brain runs an OODA cycle:

**Observe**: Collect signals from sentinels, edge agents, and connectors

**Orient**: Contextualize signals against tenant history, risk profile, and current state

**Decide**: Choose the optimal action using multi-LLM reasoning with confidence scoring

**Act**: Execute the chosen action (or request approval for high-risk actions)

Risk-Based Execution

Risk LevelBehavior

|------------|----------|

READ_ONLY / LOWAuto-execute always
MEDIUM + confidence > 85%Auto-execute
MEDIUM + confidence < 85%Require approval
HIGH / CRITICALAlways require approval
3

Signal Processing

How Signals Flow

1. **Signal Created** — A sentinel, connector, or edge agent emits a signal

2. **Signal Classified** — The Signal Processor categorizes by severity, type, and domain

3. **Signal Enriched** — Context added from tenant history and knowledge base

4. **Signal Routed** — Sent to the appropriate agent(s) for action

Signal Types

**Health signals**: System up/down, performance metrics

**Security signals**: Threat detections, policy violations

**Compliance signals**: Control pass/fail, evidence gaps

**Operational signals**: Backup status, patch availability

4

Evidence Chains

What is an Evidence Chain?

Every brain decision creates a cryptographic evidence chain:

1. **Input Evidence** — The signals and data that triggered the decision

2. **Reasoning Trace** — The AI's step-by-step reasoning (preserved for audit)

3. **Action Taken** — What was executed

4. **Outcome Evidence** — The result of the action

Why Evidence Matters

**Audit trail**: Full provenance for every automated action

**Learning**: Outcome evidence feeds back into the brain

**Compliance**: Evidence chains satisfy SOC 2 and CIS requirements

**Accountability**: Every decision traceable to specific inputs

5

Learning System

How the Brain Learns

After every decision outcome:

1. **Outcome captured**: Success, partial success, or failure

2. **Confidence adjusted**: The brain updates confidence for similar decisions

3. **Strategy reinforced or weakened**: Successful strategies get higher priority

4. **Pattern stored**: New patterns added to the brain's knowledge base

Decision Boundaries

The brain maintains decision boundaries — learned thresholds that determine when to act vs. when to request approval. These boundaries evolve over time as the brain accumulates more outcomes.

Viewing Learning Progress

Go to **Brain > Insights** to see:

Confidence trend over time

Most-learned domains

Strategy effectiveness rankings

Decision boundary evolution

Completed!

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