NightBeacon CMD
The SOC platform we built to run our own. Now you choose who runs it.
About Us
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The Attackers-Mindset
Threat-Informed Detections
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AI-driven SOC workbench
NightBeacon CMD
NightBeacon CMD is the same operating surface that Binary Defense's own analysts run their 24/7 SOC on, now in your tenant, for your team. Related alerts arrive already correlated into decision-ready situations, and roughly 90% of the investigation is done before anyone opens a ticket.
The problem we solve
Attackers outpace any human team.
Analysts face 3,000-5,000 alerts a day, and with a benign rate near 99%, the real threats wait behind the noise. You can't hire your way out of it. The only fix left is to shrink the work every alert demands.
The zero-queue model
Silence. You can trust.
With triage handled at machine speed, the queue trends toward zero, and your analysts get their time back for the work that moves the needle: hunting, tuning detections, and building playbooks. When the queue clears, the sidebar flips from a work list to seven live signals: the Peace-of-Mind panel.
Operations Room
Your whole SOC, on one screen.
A live entity map plots every asset across four quadrants (Endpoints · Identity · Data · Cloud), so an analyst clicks straight into the host, identity, or IP under attack. Alongside it runs the ranked situation queue, the AI agents on duty, and connector health, all updating in real time.
It's where every shift begins. Click any situation to start investigating on the spot, launch it straight into a hunt, or see which analyst is on call, with the team assessing and collaborating in real time, driving a faster time to response.
Situations
The whole incident, in one story.
Instead of thousands of alerts, an analyst sees a handful of situations. Open one, and the AI has already correlated 50–60 disparate alerts across endpoint, identity, network, and cloud, by shared IP, user, or asset, into one narrated situation with a confidence score, a full timeline, and an interactive attack-chain graph.
From there, the analyst follows a forward-only flow: the AI recommends the steps, proposes response actions, and surfaces tuning recommendations along the way.
Identify → Investigate → Verdict → Respond → Close
Hunt Assist
One hunt across every platform.
Type a hypothesis, pick a MITRE technique, or paste indicators. The agent translates your intent into each platform's query language and runs the hunt across every connected platform in parallel, returning one consolidated answer with a consensus score, instead of you querying each tool by hand.
Every hunt runs a goal-agent loop, plan → act → verify → iterate, with read-only queries and a strict judge that checks the evidence actually satisfies the goal. A verdict with no cited evidence is rejected; a blocked or empty hunt returns zero findings, never fabricated ones.
The set of hunt-queryable platforms is growing continuously, added on an ongoing basis, so more of the stack you already run becomes hunt-ready over time, with no rip-and-replace.
Investigations Workbench
Where the agent and analyst share one desk.
An LLM planner decomposes the situation into hypothesis steps, executes them where safe, builds a fully-sourced timeline, and proposes a verdict before the analyst opens the situation. Unlike SOAR's fixed playbook, the agent writes its own plan per incident.
Confirm, don't start from scratch → Analysts confirm decisions instead of assembling evidence.
Pivots with the attacker → When the adversary changes tack, the investigation changes with it.
One workbench, your whole stack.
Everything an analyst needs to decide and act, grounded in your data and your verdicts, and learning only from analyst decisions, never from your logs.
Deep Scan & File Analyzers
Executables, Office docs (macro detection), PDFs, SVGs, and archives get disassembly, entropy checks, and C2-capability detection natively. Deep Scan detonates the unknown in an isolated sandbox and extracts the IOCs.
Answer "is this malicious?" in-house
AI Assistant
One plain-English surface on NightBeaconAI with five modes plus Auto: Ask, Hunt, Analyze, Act, and Build. Triage, hunting, analysis, and response drafting in one place, grounded in your data and your verdicts.
5 modes + Auto
Entity Risk Radar
Every tracked host, user, and IP gets a behavioral risk score and an 8-axis spider graph that flags exactly which axes are driving the risk. The UEBA picture without the manual queries.
Behavioral risk · answered at a glance
Threat Intelligence
An aggregated surface for indicators, exploited CVEs flagged with KEV and EPSS scores, threat actors, campaigns, and feeds, with every indicator enriched against 80+ threat-intel sources, strictly passive.
What's exploited in the wild, tied to your alerts
Report Center
One-click PDF / PowerPoint / DOCX packages, with NightBeaconAI writing each report in its audience's voice (Executive, Technical, or Risk & Finance), plus a dashboard tracking hours reclaimed, dwell/MTTD, and cost avoided.
Board- and auditor-ready
Campaigns & Supply Chain
Correlate the same attacker across three or more organizations in a rolling 72-hour window, with cross-tenant privacy preserved through anonymized hashes. Supply-chain intelligence flags whether your own packages are implicated in an emerging compromise.
See it forming, before it becomes an incident
Analyze & Verdict
Verdict a URL, indicator, or payload on demand, deobfuscated, decompiled, and scored against 8,700+ malware rules, then follow the IOCs to find every other affected host in minutes.
From unknown to verdict-ready
Value Metrics
A live dashboard of NightBeaconAI's own detection and enrichment quality: true-positive rate, noise reduction, mean time to detect and resolve, SLA risk, and connector health, filterable across 7, 30, or 90 days.
The AI's performance, measured live
Get a Demo
See the zero-queue SOC in action.
The fastest way to get to see it live: thousands of alerts collapsed into a short list of situations your team can act on. Book a demo.
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Client Outcomes
What your team gets, each with the mechanism behind it.
Situations bound the queue by real events, not alert count.
Per-customer benign tuning and suppression rules surface only what's real.
ATT&CK-mapped verdicts with a full, human-signed evidence chain.
Deep Scan detonation, behavioral analysis, and multi-layer signal fusion.
High-impact actions stay analyst-approved, with a controlled blast radius.
Analyst verdicts feed continuous learning, with zero raw customer data.
Audit-ready by design
Governance you can defend.
Glass Box Visibility
Every field, attributed.
AI-generated content carries the NightBeacon mark, so analysts see what came from raw data versus the AI, and can override it. The NB Score sits beside the source severity; nothing is hidden. Your analysts never have to say "the AI said so."
Your Data Stays Yours
Never trained on your logs.
NightBeaconAI learns from analyst verdicts via synthetic data. Your data exists only in memory during analysis, never persisted for training. Zero customer data in the model, and no cross-tenant exposure.
Human Accountability
People own every decision.
AI does the analysis at machine speed; humans make every call. High-impact actions are approval-gated with a controlled blast radius, a deliberate design choice, not a limitation.
Evidence & Audit Trail
Ready for the regulator.
Every verdict carries a complete, repeatable evidence chain and a full audit trail, every query, citation, and analyst approval on record. Findings are documented and reproducible, so they hold up in front of boards, auditors, and regulators, not just the SOC.
Frequently Asked Questions.
Straight answers to what practitioners and CISOs ask most.
Does NightBeacon CMD replace SOC analysts?
No — and that's by design. NightBeacon CMD automates high-volume triage, enrichment, and first-pass classification so analysts focus on the decisions that require judgment. Human analysts retain full authority over every escalation and containment action. The goal is to make each analyst 10x more effective, not eliminate them.
Is my organization's data used to train the AI?
NightBeaconAI uses a six-layer privacy architecture that ensures zero customer data enters the training pipeline. When analysts provide feedback, a locally-hosted LLM generates synthetic variations that preserve detection patterns without any identifying details. The original event is permanently discarded.
How does NightBeacon CMD explain its verdicts?
Through LIME and SHAP token-level explanations, every classification shows exactly which words, patterns, and indicators drove the model's decision. Contrastive analysis reveals how close the call was. All findings are mapped to specific MITRE ATT&CK techniques so analysts can present evidence, not just a score.
Can NightBeacon CMD take autonomous containment actions?
No autonomous containment is permitted without human approval. NightBeacon is designed to reduce the time between alert and analyst decision — not to remove the analyst from that decision. This is a deliberate architectural choice to ensure accountability, especially in regulated environments.
How does NightBeacon CMD get smarter over time?
Through a closed-loop continuous learning system. Every analyst thumbs-up or thumbs-down is quality-scored, converted into synthetic training data, and used to retrain the model. Per-customer thresholds auto-tune to each environment's false-positive rate, so the AI reflects the specific threats targeting your customers.
How quickly can new detections reach production?
Under 10 minutes. NightBeacon CMD's modular analyzer architecture means new detection patterns added to a specific analyzer module propagate automatically throughout the full pipeline — risk scoring, API, UI, explanation generation. No release trains. No sprints. Research-to-production at the speed threat intelligence is published.
What makes NightBeacon CMD different from other AI SOC Platforms
NightBeacon CMD was built inside a live MDR operation — not in a lab. It optimizes for trust, explainability, and sustained decision quality rather than autonomous headcount replacement. Competitors may offer more autonomy; NightBeacon CMD offers more accountability — a meaningful distinction for regulated industries.
How does the zero-queue model actually work?
NightBeacon CMD auto-correlates related alarms across endpoint, identity, network, and cloud into a single situation before anyone opens a ticket, with enrichment and verdicts already done. Benign activity resolves automatically, so the queue trends toward zero, and analysts work a short list of situations instead of thousands of alerts.
Get a Demo
Our workbench, inside your walls.
Related alerts arrive already correlated into decision-ready situations, so your analysts stop assembling evidence and start hunting, and every verdict carries the reasoning to back it. Faster decisions, fewer missed threats, and AI you can always explain.