Modern cyber threats are multi-stage, coordinated attacks. But most security tools treat them as isolated incidents, so defenders can’t see the whole picture of an attack. Cybersecurity analytics bridges that gap by correlating data across systems, timeframes and entities to show patterns that would otherwise be hidden. It lets security teams detect earlier, respond better and adapt to evolving attack tactics. That’s more than just reviewing disconnected logs. It’s understanding how malicious domains, compromised endpoints and suspicious behavior are connected in a bigger campaign.

In this article we’ll explore the foundations of cybersecurity analytics, the importance of connected data and how graph-based techniques can bring order to chaos in threat environments. We’ll also show this in practice by using PuppyGraph and threat intelligence from Open Threat Exchange (OTX) to visualize relationships and speed up threat hunting.

What is Cybersecurity Analytics?

Cybersecurity analytics is the practice of collecting, correlating, and analyzing security data to detect, investigate, and respond to threats with greater intelligence. Instead of reacting to isolated alerts, it empowers security teams to uncover hidden patterns, trace attack paths, and predict future risks based on connected evidence.

Modern environments produce massive amounts of telemetry, from system logs and network flows to cloud service events and user activities. Cybersecurity analytics transforms this raw data into structured insights that power faster, more accurate decisions.

How Cybersecurity Analytics Works

At its core, cybersecurity analytics supports four goals: detection, prevention, response, and prediction.

Detection identifies threats as they emerge. Prevention uses past learnings to strengthen defenses. Response provides the context needed to contain incidents effectively. Prediction anticipates vulnerabilities and attack strategies before they are exploited.

The need for analytics has become clear as traditional security tools show their limits. Signature-based detection, static rules, and isolated log reviews often fail against today’s stealthy, multi-stage attacks. Defenders must see not just individual events, but the relationships between them. Analytics provides the ability to connect dots across time, systems, and identities, revealing coordinated activity that would otherwise go unnoticed.

The Analytics Lifecycle

Effective cybersecurity analytics follows a structured process that transforms noisy telemetry into actionable insights.

Each stage must work efficiently with the others. Gaps or delays at any point in the lifecycle can break the flow of intelligence and delay critical threat detection.

Cybersecurity Analytics vs General Data Analytics

Cybersecurity analytics is a subset of data analytics, and they often overlap. Security teams need strong analysis, and data teams need basic security know-how. But, there are also subtle differences between the two that can influence how problems are approached. 

Goals

Cybersecurity analytics aims to protect confidentiality, integrity, and availability. It reduces risk by finding exposures and guiding remediation, and it supports incident response with clear actions. Common questions include: Is this behavior malicious? How did access propagate? Which assets are affected? What should we block, isolate, revoke, or patch first? 

By contrast, general data analytics improves business performance across revenue, cost, and customer experience. It informs product and operational decisions and supports planning and forecasting. Common questions include: Which segments are churning? What drives conversion? Where can we reduce cost without hurting outcomes? How should we allocate budget next quarter?

Data Sources

Cybersecurity analytics relies on telemetry across infrastructure, endpoints, identities, and cloud services. Key sources include operating system and application logs, firewalls and network devices with flow records and packet captures, EDR process and file events, identity provider authentication and session data, and cloud audit logs such as AWS CloudTrail, Azure Monitor, and Google Cloud Audit Logs. Risk and threat intelligence complements these with vulnerability scan results and curated indicators from feeds such as OTX.

General data analytics draws on product events, transactions, catalog and pricing data, CRM and marketing campaigns, support tickets, finance and supply chain records, and survey research. These datasets are modeled in a warehouse or lakehouse to support reporting, experimentation, and forecasting.

Time Sensitivity

Cybersecurity analytics is latency sensitive. Many signals lose value within minutes, so pipelines favor streaming or near-real-time collection. Teams keep hot, searchable storage for recent weeks to speed investigations, then archive raw events for months or years for forensics and compliance. Typical cadences: identity and cloud audit in near real time, endpoint and network in near real time, vulnerability and configuration daily to weekly, threat intelligence continuous.

General data analytics aligns freshness with decision cadence. Daily or weekly batches are standard, with real time reserved for personalization or operational alerts. Retention favors longer histories at lower granularity for trends and planning. Accuracy and statistical confidence often outweigh immediacy, so modest delays are acceptable when they improve data quality and consistency.

Types of Cybersecurity Analytics

Threat Detection Analytics

Threat detection analytics identifies malicious activity by analyzing logs, network traffic, and endpoint events for suspicious patterns or known attack signatures. This approach is central to spotting intrusions early, whether it’s detecting unusual data transfers, unauthorized access attempts, or malware communication with command-and-control servers.

Behavioral Analytics

Behavioral analytics establishes a baseline of normal user, device, or application activity, then alerts when deviations occur. By focusing on how entities usually operate, this method can surface threats that blend in with legitimate traffic, such as insider threats, compromised accounts, or subtle data exfiltration.

Incident Response Analytics

Incident response analytics helps security teams investigate and contain threats quickly by correlating related alerts, prioritizing them based on severity, and providing relevant context. By turning raw events into actionable incidents, this approach streamlines response workflows and reduces the time attackers have to operate in an environment.

Vulnerability Analytics

Vulnerability analytics ranks weaknesses in software, systems, and configurations so teams fix the highest-risk issues first. It draws on scanner results, SBOMs, configuration baselines, patch histories, exploit intelligence, and asset criticality to estimate exploitability and impact. The output is a prioritized remediation backlog with fix-by targets and SLAs, typically updated on a daily or weekly cycle. Unlike threat detection, it addresses latent risk rather than live attack activity.

Predictive Analytics

Predictive analytics uses historical data, threat trends, and real-time telemetry to forecast where and how future attacks might occur. By spotting patterns that precede incidents, it helps security teams take proactive measures, such as tightening access controls or increasing monitoring on high-risk assets.

Benefits of Cybersecurity Analytics

Cybersecurity analytics strengthens an organization’s security posture by transforming raw security data into actionable insight. Its benefits extend beyond individual tools or workflows and apply across the entire security program.

Early Threat Detection

Unifying telemetry across endpoints, identities, networks, apps, and cloud lets you spot trouble sooner and in more places. Streaming collection and simple baselines make odd behavior pop quickly, which cuts dwell time. Without these analytics, siloed tools and static thresholds tend to catch only the obvious and miss slow, cross-system activity.

High-Confidence Alerting

Normalization, enrichment, and correlation turn raw events into clear alerts with the context analysts need to act. As rules and models adapt to feedback, precision improves, triage speeds up, and false positives drop. With security logs pouring in at high volume and velocity, cybersecurity analytics cuts through the noise so teams can focus on the alerts that truly matter, easing alert fatigue.

Accelerated Incident Response

In cybersecurity, every minute counts. Linked events form timelines and likely attack paths, so responders see scope, impact, and next steps at a glance. Automated enrichment and playbooks help isolate affected systems, limit lateral movement, and bring MTTR down. Getting the right context quickly, without bouncing between consoles or rebuilding timelines by hand, can be the difference between stopping an attack early and facing a full-scale breach.

Risk-First Remediation

Vulnerabilities and misconfigurations are ranked by exploitability and business impact, producing a focused backlog with clear fix-by targets and SLAs. By tackling the highest-risk issues first, each patch cycle delivers a measurable reduction in overall risk.

Real-World Applications

Cybersecurity analytics is most effective when mapped to the problems security teams face. Organizing these solutions by the challenges they solve highlights their practical value in day-to-day operations.

Compromised Account Detection

By analyzing login patterns, device fingerprints, and session activity, analytics can flag account takeovers before they spiral into bigger problems. Signals like impossible travel, sudden location changes, or unusual login times stand out quickly, so teams can lock accounts and limit damage fast.

Data Exfiltration Monitoring

Outbound traffic, SaaS exports, and object storage access are tracked for signs of sensitive data leaving the environment. Unusual data volumes or destinations outside normal patterns prompt quick action to secure the information before it’s lost.

Cloud Misconfiguration Detection

Cloud audit logs, IAM permissions, and resource configurations are continuously checked for risky exposures. Publicly accessible storage or overly permissive roles are flagged, so teams can fix them before attackers find and exploit them.

Vulnerability Prioritization

Scanner results, exploit intelligence, and asset criticality are combined to focus attention where it matters most. Threat feeds like OTX pulses add context by highlighting vulnerabilities under active exploitation, ensuring those risks move to the top of the backlog and get addressed before attackers can take advantage.

Key Challenges

Modern security environments generate massive volumes of telemetry, events, and alerts. Yet context—the understanding of how individual events relate to one another—remains scarce. Each log entry often captures only a fragment of the broader story, forcing defenders to react to symptoms rather than address root causes.

Attacks rarely unfold in a simple, linear fashion. A single campaign might involve phishing emails, credential theft, lateral movement, malware deployment, and cloud resource abuse, with each phase leaving traces across different systems. For example, one endpoint might generate a malware hash tied to a domain, another device might beacon to that domain, and a cloud instance might later communicate with the same attacker infrastructure. Traditional security tooling often treats these signals as isolated events, missing the larger patterns that link them together.

The difficulty stems from how most analytics systems structure data. Platforms based on static tables and schemas excel at point-in-time queries but struggle to model dynamic, multi-hop relationships. Join-heavy queries across disparate datasets are slow and unreliable. Recursive queries, which are critical for tracing attacker movement across multiple assets, are difficult or unsupported. As a result, analysts are often forced to manually pivot between dashboards and tools, reconstructing the attack graph mentally during investigations.

This fragmentation creates serious blind spots. Without a way to natively model and explore relationships, defenders risk missing key links between events. Investigative questions such as which assets communicated with a suspicious domain, which users accessed the same datastore, or whether lateral movement occurred are fundamentally about relationships, not isolated records.

Addressing this challenge requires shifting from thinking in terms of individual events to modeling connections. Graph-based approaches, which treat entities and relationships as first-class citizens, provide a more natural and powerful way to reconstruct attacks, uncover hidden patterns, and respond with greater speed and clarity.

Getting Started with Cybersecurity Analytics

A graph model represents data as entities (nodes) and relationships (edges) between them. In cybersecurity, nodes might represent users, endpoints, domains, IP addresses, or file hashes, while edges capture actions and associations such as logins, communications, or malware relationships. This structure makes it easier to query, visualize, and understand how threats spread across complex environments.

To make graph modeling practical for security teams without complex infrastructure changes, PuppyGraph provides an accessible and scalable solution.

PuppyGraph offers a real-time, zero-ETL graph query engine that allows organizations to query existing relational data stores as unified graphs without moving or duplicating data. By connecting directly to SQL-based systems, PuppyGraph enables teams to model and explore relationships through familiar languages such as openCypher and Gremlin, without restructuring their underlying databases.

Unlike traditional graph databases, PuppyGraph eliminates the need for complex ETL pipelines and specialized storage. It supports petabyte-scale datasets and fast, multi-hop queries through a distributed, vectorized execution engine, with separate computation and storage layers to maintain consistent performance as data volumes grow.

By simplifying graph analytics over existing infrastructure, PuppyGraph helps security teams uncover hidden patterns, map attack paths, and accelerate threat investigations. To demonstrate this approach, we will walk through an example using PuppyGraph and threat intelligence data from Open Threat Exchange (OTX).

Demo

This demonstration shows how PuppyGraph can be used to model and analyze real-world threat intelligence data from Open Threat Exchange (OTX). We transform OTX pulses—summaries of threats and associated indicators of compromise (IOCs)—into a graph structure for querying and visualization. The OTX data is downloaded as JSON files, imported into a PostgreSQL database, and then mapped into a graph model using PuppyGraph. Pulses group related IOCs, providing context about threat campaigns, malware families, or attacker infrastructure.

To help you follow along, we have prepared all necessary materials, including setup scripts, schema files, and sample code, in a public GitHub repository. Please download or clone the repository before starting.

Environment Setup

1. To follow along, you will need Docker Compose, Python 3, and an OTX API key. Start by launching PostgreSQL and PuppyGraph services:

docker compose up -d

2. Next, create a Python virtual environment, activate it, and install the required dependencies:

python3 -m venv myvenv
source myvenv/bin/activate
pip install psycopg2-binary

3. Install the OTXv2 Python SDK from the customized repository:

cd ../OTX-Python-SDK  
pip install .

4. After installation, navigate back to the demo directory.

cd ../demo-1

Importing OTX Data

1. Configure your OTX API key in data.py and download threat pulses:

python data.py download

2. Access the PostgreSQL client (Password: postgres123.) and create the required tables:

docker exec -it postgres psql -h postgres -U postgres

Then run the SQL commands in create_tables.sql to set up the schema.

3. Import the downloaded data into PostgreSQL:

python data.py import

4. Access the PostgreSQL client as before and run some queries to verify the data:

SELECT * FROM pulse LIMIT 5;

Building the Graph Model in PuppyGraph

Access the PuppyGraph Web UI at http://localhost:8081 using:

• Username: puppygraph
  
  
• Password: puppygraph123
  
  

Upload the provided schema.json file through the Upload Graph Schema section to define the nodes and edges.

Querying Threat Relationships

In the PuppyGraph Query interface, you can run Gremlin or openCypher queries to explore the relationships between pulses and indicators. Here are some example queries of Gremlin and Cypher. 

Gremlin queries:

// Count the number of pulses
g.V().hasLabel("pulse").count()

// Maximum number of indicators linked to a pulse
g.V().hasLabel("pulse").local(__.out("pulse_indicator").count()).max()

// Top 10 pulses by number of indicators
g.V().hasLabel('pulse').as('p').
  project('name', 'description', 'indicatorCount').
    by('name').
    by('description').
    by(__.out('pulse_indicator').count()).
  order().by(select('indicatorCount'), desc).
  limit(10)

// Indicators linked to two or more pulses
g.V().hasLabel("indicator").
  where(__.in("pulse_indicator").count().is(gte(2))).
  in("pulse_indicator").path()

Cypher queries:

// Count the number of pulses
MATCH (n:pulse) RETURN COUNT(n)

// Maximum number of indicators linked to a pulse
MATCH (p:pulse)
OPTIONAL MATCH (p)-[:pulse_indicator]->(i)
WITH p, COUNT(i) AS indicatorCount
RETURN max(indicatorCount) AS maxIndicatorCount

// Top 10 pulses by number of indicators
MATCH (p:pulse)
OPTIONAL MATCH (p)-[:pulse_indicator]->(i)
WITH p, COUNT(i) AS indicatorCount
RETURN p.name, p.description, indicatorCount
ORDER BY indicatorCount DESC
LIMIT 10

// Indicators linked to two or more pulses
MATCH (i:indicator)<-[:pulse_indicator]-(p:pulse)
WITH i, COUNT(p) AS pulseCount
WHERE pulseCount >= 2
MATCH path = (p)-[:pulse_indicator]->(i)
RETURN path

Cleanup

When finished, shut down and remove the running services:

docker compose down -v

Conclusion

Today’s cyber threats rarely stay confined to a single system or user. They move across devices, identities, and cloud services, making traditional alert-based defenses insufficient. To respond effectively, security teams need correlation, context, and clarity—qualities that cybersecurity analytics brings together to detect faster, investigate deeper, and act smarter.

But volume alone isn’t enough. Real value comes from modeling and exploring the relationships hidden in the data. That’s why many teams are turning to graph-powered approaches to trace attacker infrastructure, map lateral movement, and connect the dots efficiently.

If you’re ready to go beyond dashboards and uncover deeper structure in your threat data, try the forever-free Developer Edition or book a demo with our team.