AI Baselining Diagnostics
Heartbeat every asset. Detect change. Quantify risk.
Ultimate Network and Cloud Visibility. CySight uses Machine Learning and predictive AI baselining to learn normal behavior per asset and service, then measures what changed, when it started, and how serious it is. Diagnose DDoS, misuse, and low and slow anomalies with full fidelity flow evidence, even in encrypted and segmented environments.
CySight = Foresight from Hindsight + Insight from Deepsight
Deep tech that makes full use of granular flow visibility and scales up or down to your architecture without losing proof.
Per asset baselines
Heartbeat learning per service footprint
Quantified change
Deviation and seriousness, not guesswork
Evidence intact
Retain the flow detail others discard
Predictive baselining turns hindsight into foresight by measuring behavioral change with retained proof.
Turn anomalies into quantified risk and proof
Most anomaly tooling shows spikes but cannot explain what changed, cannot prove scope, and cannot hold up under scale. CySight baselines every asset, preserves full fidelity flow evidence, and turns abnormal traffic into measurable deviation, accountable ownership, and defensible reporting.
DDoS diagnosis with evidence
Confirm whether a flood is real, where it started, and what it impacted by comparing behavior against learned baselines and retained flow context.
Detect low and slow anomalies
Surface subtle shifts that bypass thresholds by measuring change across comparative windows and per asset heartbeat patterns.
Quantify seriousness and blast radius
Attribute deviation to the entities that drove it and qualify seriousness using ports, protocols, direction, and segmentation context.
Reduce alert fatigue
Replace noisy threshold storms with baseline driven deviation scoring and repeat pattern learning tied to real evidence.
AI baselining and ML diagnostics that go beyond DDoS
This is not another point solution. CySight is a baseline engine built on granular flow visibility that learns behavior per asset and service, then converts change into measurable diagnostics, scope, and risk with evidence grade context.
Per asset predictive baselining
Continuously learns normal behavior per asset and service footprint so unusual activity is measurable and repeatable.
Comparative time windows
Compare minute, hour, weekday, and monthly behavior to pinpoint when change began and whether it is sustained or transient.
Change attribution
Identify which endpoints, services, and organizational units drove the shift, not just that a chart moved.
Pattern qualification
Differentiate floods, bursts, scanning, misuse, and abnormal service behavior using direction, ports, protocols, and context.
Risk and accountability context
Map IP ranges and allocations to tenants, accounts, cost centers, and business units so impact and ownership are provable.
Optional vector store and SLM
Link related signals across time and entities and generate clear evidence grounded explanations, while detection remains baseline driven.
Evidence grade retention
Preserve the flow detail needed to reproduce what happened and support response, compliance, and audit defensibility.
Built to scale
Unsurpassed collection, correlation, granular retention, and diagnostics to extract deep intelligence from huge flow data under pressure.
Open workflow - from heartbeat to proof
CySight keeps investigative scope as you pivot. Start anywhere, validate with baselines, quantify the change, and export defensible proof without losing context.
AI baselining Heartbeat learning
Learn normal behavior per asset and service footprint, then score deviation so unusual activity is measurable, comparable, and repeatable with evidence grade context.
Detect deviation Surface the shift
Identify bursts, floods, and subtle drift by measuring change against the learned baseline instead of relying on static thresholds.
Explain what changed Compare windows
Compare minute vs recent minutes, hour vs recent hours, weekday vs historical weekdays, and month vs historical months to pinpoint when the shift started and which entity drove it.
Qualify seriousness Context and direction
Distinguish external floods, internal misuse, and lateral anomalies by correlating direction, ports, protocols, endpoints, and segmentation boundaries.
Prove scope and ownership Segmentation mapping
Tie anomalies to accounts, cost centers, business units, and boundaries so response actions and reporting are accountable and defensible.
Operationalize outcomes Exports and integrations
Schedule reports, export in standard formats, and forward high fidelity alerts to SIEM, SOAR, XDR, or policy tooling when required.
Deep dive - AI baselining diagnostics feature set
Ultimate Network and Cloud Visibility Full fidelity flow analytics
- Use granular flow visibility as the primary evidence source across network and cloud architectures.
- Scale up or down to match your environment while preserving investigative continuity.
- Keep visibility even where encryption and segmentation break probe based assumptions.
Predictive AI baselining Heartbeat every asset
- Continuously learn behavior per asset and service footprint across time windows.
- Score deviation so change is measurable, comparable, and defensible.
- Optional vector store and SLM can link related signals and produce clear explanations without changing detection logic.
DDoS and burst diagnostics Volume, rate, record explosions
- Identify floods and abnormal bursts using count style analytics, rate shifts, and comparative timelines.
- Confirm whether impact is inbound, outbound, internal, or lateral by correlating direction and context.
- Preserve the retained detail needed for response narratives and post incident proof.
Anomaly qualification What it is, not just that it exists
- Categorize abnormal traffic patterns using ports, protocols, endpoints, direction, and segmentation context.
- Expose misuse and stealth anomalies that are invisible to static thresholds.
- Quantify seriousness by tying deviation to the entities that drove the change.
Reporting and alerting Fast outputs teams can use
- Generate high speed contextual network traffic accounting, reporting, and alerting.
- Export in formats used by operations, SOC, and compliance workflows.
- Build evidence grade narratives from retained telemetry, not partial summaries.
AI Baselining Diagnostics - evaluation pack
Use this overview for evaluation and internal alignment on how CySight baselines every asset, measures change, and turns anomalies into quantified diagnostics and proof.