Security decisions atmachine speed.Powered by AI reasoning.
Spakto's AI Decision Layer applies large language model reasoning to live attack graph data — autonomously analyzing attacker intent, predicting next steps, and prioritizing response actions with explainable, auditable logic.
Not pattern matching.
Contextual reasoning.
Traditional ML flags anomalies. Spakto's AI layer understands attacker intent — reasoning about why an attacker is doing something, predicting what comes next, and recommending what to do about it. Every decision is explainable, auditable, and human-overridable.
The AI
Reasoning Loop
Five stages run continuously — the AI observes the live attack graph, reasons about what it means, predicts what comes next, prioritizes response, and learns from analyst feedback. The loop never stops.
Graph Event Observation
- ›Live attack graph signal ingestion
- ›New node and edge detection
- ›Behavioral baseline comparison
- ›Anomaly flagging for AI analysis
LLM Contextual Analysis
- ›Security domain knowledge injection
- ›Cross-signal context assembly
- ›Attacker intent inference
- ›MITRE ATT&CK technique mapping
Next-Step Forecasting
- ›Kill chain progression modeling
- ›Likely-path probability scoring
- ›Crown jewel exposure forecast
- ›Attacker goal hypothesis generation
Risk-Based Ranking
- ›Business impact weighting
- ›Asset criticality scoring
- ›Time-to-breach estimation
- ›Response urgency classification
Feedback Integration
- ›Analyst accept/override signals
- ›False positive rate reduction
- ›Environment-specific tuning
- ›Model accuracy improvement loop
Why LLM reasoning
outperforms ML detection
Traditional ML matches patterns. LLM-based AI reasons about attacker intent, behavior, and context — catching what pattern-matching systems can never see.
What the AI
decides for you
Four core AI capabilities run continuously across the live attack graph — each returning structured, explainable, auditable output that feeds directly into analyst workflow.
Ranks by attacker progression, not severity
Traditional scoring ranks by CVSS. Spakto AI ranks by how far along the kill chain the attacker already is, what assets are at risk, and how quickly they can reach crown jewels.
Forecasts next attacker steps before execution
Using adversarial reasoning models trained on thousands of attack chains, the AI predicts likely next moves with confidence scores — giving responders minutes of advance warning.
Creates step-by-step response with full context
Every high-confidence finding triggers automatic playbook generation — specific containment steps, network isolation commands, IAM revocation steps — ready for analyst review.
Translates findings into board-ready language
One click converts the full technical attack chain into a plain-language board report — risk impact, business exposure, and recommended actions in C-suite language.
Every decision.
Fully explained.
Black-box AI in security is unacceptable. Every Spakto decision comes with a complete evidence chain, confidence score, step-by-step reasoning trace, and human override capability.
Every signal used in the decision — timestamped, sourced, and mapped to MITRE ATT&CK. No black-box inputs.
0-100% score with full explanation of contributing factors, alternative hypotheses, and uncertainty sources.
Step-by-step logic the AI applied — each inference explicit, reviewable, and auditable for compliance.
One-click override on any AI decision. Overrides feed the learning loop — the AI improves from corrections.
Transform your security
team's impact
From overwhelmed SOC teams drowning in alerts to IR teams racing to reconstruct attacks — the AI Decision Layer transforms every security function.
Stop manually triaging thousands of daily alerts. The AI handles tier-1 triage, correlates signals into attack chains, and surfaces only what needs human attention — with full context and suggested response.
- ›Automated tier-1 triage
- ›Pre-analyzed attack chains
- ›AI-suggested playbooks
- ›Analyst focus on complex work
AI generates plain-language risk narratives from technical attack graph findings — business impact, executive summary, and recommended actions — without manual translation by senior analysts.
- ›Automatic executive translation
- ›Business impact framing
- ›Risk severity in C-suite language
- ›Full technical appendix available
AI predicts lateral movement paths before they complete, provides step-by-step response playbooks with full context, and gives responders a complete correlated timeline from the moment a case is opened.
- ›Predictive lateral movement alerts
- ›Automated response playbooks
- ›Full correlated IR timeline
- ›Patient-zero identification
The inference
engine architecture
A 4-layer inference stack processes live attack graph data through context assembly, LLM reasoning, and structured decision output — all within your deployment boundary, no external API calls.
- ›JSON-structured findings with confidence
- ›Natural language narratives for any audience
- ›Step-by-step response playbooks
- ›Human review queue with override support
- ›Compliance-ready audit trail
- ›Security-domain fine-tuned LLM
- ›Full context window over attack graph
- ›On-premises or VPC-isolated inference
- ›No raw telemetry to external APIs
- ›<2 second end-to-end latency
- ›Correlated attack graph as structured input
- ›MITRE ATT&CK enrichment injection
- ›Asset criticality and exposure context
- ›Historical attacker pattern retrieval
- ›Dynamic context window optimization
- ›Live attack graph event stream
- ›50+ source normalized telemetry
- ›Real-time entity resolution output
- ›Cross-domain correlation results
- ›Streaming ingestion, zero batch
Predict the attacker's
next move before it happens
For each active attack technique, the AI predicts the most likely next MITRE ATT&CK technique — giving your team advance warning before the attacker reaches the next stage.
AI Decision Layer FAQs
Frequently asked
questions.
answered
It's a reasoning engine built on large language models that sits above Spakto's live attack graph. Instead of flagging raw alerts, it analyzes attacker behavior in context — inferring intent, predicting next moves, and generating prioritized, explainable response actions.
The AI autonomously handles triage, prioritization, and playbook generation, but any containment or response actions require human analyst review. Every AI output includes a human override option and a full reasoning trace so analysts stay in control.
Analyst feedback on false positives is fed back into the reasoning model as part of a continuous learning loop. The AI improves its accuracy over time, tuned specifically to your environment rather than generic threat models.
Yes. All AI inference runs within your deployment boundary. No raw security telemetry is sent to external LLM APIs. Spakto supports on-premises and VPC-isolated deployments for organizations with strict data residency requirements.
Traditional ML detects anomalies by pattern matching against historical data. Spakto's AI layer applies contextual reasoning — understanding why an attacker is doing something, predicting what comes next, and providing evidence-backed explanations, not black-box scores.
Every decision includes an evidence chain (which signals it used), a confidence score, a step-by-step reasoning trace, and the alternative hypotheses it considered. There are no black-box outputs — every conclusion is fully auditable.
The AI reasons over the live attack graph — which includes correlated EDR, IAM, cloud, and network signals — enriched with MITRE ATT&CK context, asset criticality, and historical attacker behavior patterns specific to your environment.