Flow Forensics at Full Fidelity

Consume metadata at scale. Retain the granularity that forensics requires.

CySight captures, normalizes, enriches, and retains high volume context and metadata and flow across vendors and environments. Investigate encrypted and segmented networks without relying on packet decryption, including slow burn leaks and long running breaches.

Explore Flow Security

Why this matters

Networks and clouds are converging. Traffic volumes are exploding. Encryption is the default. Compliance pressure is rising. Many stacks survive by reducing telemetry. CySight is designed to keep granularity so investigation, audit, and accountability remain possible days, weeks, and months later.

One evidence layer across Threat, Baseline, Performance, and Forensics

The same retained flow evidence powers threat detection, behavioral baselines, performance visibility, and forensic traceability. You do not switch data sources or lose fidelity when you switch perspectives.

LateralConnect identities, assets, events, and risk to expose lateral movement paths, quantify impact, and reveal hidden relationships across the environment.

ThreatDetect attack patterns and validate indicators with retained evidence.

BaselineLearn periodic behavior and expose drift, outliers, and covert change.

PerformanceTrack utilization, latency, drops, and flow behavior without blind spots.

ForensicsInvestigate quickly, then prove control with long term traceability.

Full visibility, enrichment, baselines, threat correlation, and forensics in one platform.

Security grade visibility without the cost and complexity

Large environments generate massive volumes of flow metadata. Traditional approaches either miss most of it or become too expensive to retain, search, and explain. CySight is designed to retain big data in a small footprint and keep the granularity that real investigations, compliance, and accountability require.

1) Ingest at enterprise scale

Collect flow metadata across vendors and distributed environments, including high rate collectors. Keep what the device provides, then standardize it so analytics stay consistent.

High volume ingestion, multi tenant, IPv4 and IPv6
Consistent normalized fields for comparable analytics
Long term retention without discarding fidelity

2) Identity learning and attribution

Learn identities from sources such as RADIUS and LDAP, and from tools that integrate with them and provide APIs. Apply learned identity context across conversations to strengthen triage and accountability.

User and device attribution for investigation
Consistent identity labels across time ranges
Supports deeper context where available

3) Geo and regional attribution

Geo and regional attribution is a default lens for triage. If additional regional context is added, it increases clarity and speeds prioritization. This strengthens conclusions without requiring payload inspection.

Fast triage by country, region, and boundary
Clearer prioritization for suspicious patterns
Supports segmentation and accountability

4) Vector connectivity and joined context

Connect the dots across assets, identities, applications, peers, threats, and events. Use the vector store to extend relationship discovery and continuously improve investigation quality as CySight lands and expands.

Lateral connectivity across learned contexts
Joined context for faster investigation and reporting
Designed to extend as new sources are integrated

5) AI event baselining, discovery, and triage

Baselining is not separate from forensics. CySight uses behavioral baselines to generate events, discover deviation, and triage what matters. Events carry quantified context to support clear decisions and audit trails.

Event driven forensics with evidence
Quantified change across time windows
Supports lateral movement discovery

6) Zero Trust risk loop

Zero Trust is strengthened when risk is measurable and traceable. CySight quantifies risk in events, then feeds joined context and vector connectivity back into the detection cycle to keep baselines and triage sharp.

Risk tracked across multiple impacts
Clear accountability by asset, identity, and boundary
Evidence supports compliance and insurance narratives

Core forensic lenses

These lenses are designed for speed, clarity, and long term traceability. Combine them across any field set and any time range, then pivot instantly to explain what changed, who was impacted, and what the evidence shows.

CountExpose scanners, spammy conversations, long lasting flows, and sudden change.

PercentileShort and long term threshold shifts for security and billing evidence.

Top X/YFlexible aggregation across peers, ASNs, threats, assets, and applications.

DeviationSurface what changed most - outliers, floods, worms, and DDoS indicators.

BaselineCompare periodic behavior across timelines for any element or grouping.

Bi directionalForward and reverse flows to pinpoint responsibility and directionality.

Cross sectionStacked comparisons for multi parameter analysis and QoS context.

Custom groupsGroup by site, customer, app, service, business unit, or cost center.

Event forensicsInvestigate from AI generated events, then drill into the exact evidence behind them.

Joined contextsCombine identity, geo, asset, threat, and business context into one view.

Zero Trust lensTrack exposure and accountability by boundary, segmentation, and access paths.

Vector lateral viewDiscover relationships and lateral movement signals across connected contexts.

Designed to feel like your Threat, Baseline, and Performance views: the same evidence layer, multiple perspectives, consistent drill down.

Supported flow structures

CySight supports multiple flow structures across vendors and environments. Each structure includes a defined field set, normalized mappings, and extension points for enrichment and joined context.

Open Flow Structures and Fields

CySight Stack reference

The CySight stack is the operating model for security, IT, and compliance teams: collection, normalization, enrichment, AI baselining, threat correlation, forensics, and response workflows.

One platform - one evidence layer - multiple perspectives.

Collect flow and log metadata at volume, across vendors and environments
Normalize into consistent structures so analysis stays comparable
Enrich with identity, geo, threat intelligence, and business context
Baseline every asset and service behavior to detect drift and covert patterns
Investigate with forensic lenses built for speed and long term traceability
Respond by exporting evidence and alerts to your policy manager, XDR, or SIEM

Proof in the UI

The forensics experience is designed to match the strength of Threat, Baseline, and Performance views. The Daashboard and UI is where joined context, event triage, and rapid drill down become operational.

Flow Structures - Evidence table

High density forensics view designed for investigation speed: entities, ports, deviation, snapshots, direction, boundary, and event category.

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Unified Threat and Risk Landscape

Joined context view that brings together groups, assets, services, countries, and external IPs, with event driven exploration and rapid triage.

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What these views enable

AI event baselining and discovery that produces investigation ready events
Joined contexts that connect identity, asset, geo, threat, and business grouping
Zero Trust lens that tracks boundary behavior and accountability across time
Vector lateral connectivity that reveals relationships and movement paths
Quantified risk recorded in events, then fed back into the detection cycle

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