Today’s security teams need to effectively turn large datasets into actionable insights, quickly deliver features to streamline and automate security operations, and confidently ensure high software reliability and scalability. Cybersecurity applications help security teams stay ahead of bad actors, protecting organizations from the debilitating effects of a cyberattack. Next-generation cybersecurity applications are designed to connect directly to the customer’s security data lake. In this article, we’ll explore the main types of cybersecurity applications, sharing examples of each. We’ll discuss the importance of expanding your cybersecurity data sources and share how the security data lake can unify this data into a single source of truth.  

Primary Categories of Cybersecurity Applications

Cybersecurity is a multifaceted discipline. The many different types of cybersecurity applications reflect this, with each designed to strengthen a specific aspect of the organization’s overall security stance. 

SIEM 

Security information and event management (SIEM) solutions help security teams with compliance, threat detection and security incident management, by gathering and studying past and present security events, along with several other data sources. The main capabilities of a SIEM application include collecting and organizing log events, analyzing these events and data from different sources, and assisting with tasks like threat detection, incident response and report creation.

Cloud security 

In most instances, cloud providers retain responsibility for securing the cloud itself, along with related infrastructure. This arrangement leaves the customers responsible for securing the assets they have in the cloud, including data, code and other digital assets. Cloud security applications, such as a cloud security posture management (CSPM) application, automate the identification and resolution of misconfigurations that can leave an organization's cloud-based assets vulnerable to attack. This provides guided remediation support to ensure the identified configuration issues  are fully resolved.

Governance, risk and compliance

Governance, risk and compliance (GRC) technology is used by organizations to structure processes that protect their data and assets through governance, risk management and regulatory compliance practices. Because changes in regulations and threats happen frequently, security leaders need tools that evolve with these changes to keep threats at bay. 

Endpoint security 

Endpoints such as mobile phones, laptops and servers are common gateways into an organization’s network. Endpoint security applications protect these access points from compromise, automatically detecting, analyzing, blocking or containing cyberattacks. Endpoint detection and response (EDR) systems are one example. These cybersecurity applications combine real-time, continuous monitoring and data collection with analysis and automated response, helping security teams quickly identify, contain and remove endpoint threats.

Identity and access management 

Identity and access management (IAM) applications regulate user access to resources, automatically authenticating and authorizing users before providing them with access to the information and systems required to complete their work. An IAM method such as role-based access control (RBAC) restricts access to network resources according to the person’s role within the organization, rather than the person individually. This technique is especially well-suited to larger organizations where controlling user access at the individual level is not practical. Cybersecurity applications that use RBAC allow administrators to create user groups with similar permissions and rights, and assign roles and responsibilities that provide access to a specific set of resources. 

Data security 

Data security applications protect an organization’s data during storage and transit. They safeguard the confidentiality, integrity and availability of business data, monitoring for and preventing the unsafe or unauthorized use, sharing or exfiltration of sensitive data. Data loss prevention (DLP) tools continuously monitor the data coming into a business network and then leaving it, automatically blocking suspicious or unauthorized activities such as uploading business data to a consumer cloud storage service. These cybersecurity applications use machine learning to uncover abnormal patterns of behavior that may indicate the presence of an insider threat.

Leading Sources of Cybersecurity Data

Ready access to diverse, up-to-date threat intelligence data is essential for detecting and responding to the latest cyber threats. These primary sources provide information on cyber risks, attacks and vulnerabilities, helping security teams stay informed on the evolving threat landscape.

Network traffic data

Network traffic data provides visibility into the activity occurring on the organization’s network, helping security teams uncover and respond to suspicious activity. Cybersecurity applications that focus on network security collect and analyze data sources including netflow records, firewall and proxy logs, and full packet captures. 

Vulnerability data

Vulnerability data, generated from a vulnerability assessment, highlights security weaknesses in an organization’s operating systems, software applications, networks and other digital infrastructure. Cybersecurity applications that conduct this type of assessment help security teams better understand their attack surface, allowing them to proactively address vulnerabilities before they can be exploited. Examples of actionable data include Common Vulnerability and Exposures (CVE), patch management tracking, and open ports and services.

Threat intelligence

Threat intelligence data provides detailed information about new and emerging cyber threats. This information includes recently identified ransomware or malware, indicators of compromise (IOCs), and new tactics or techniques being used by hackers. This type of data is often gathered from threat feeds offered by cybersecurity companies, government agencies and other third-party providers.

Incident data

Incident data helps organizations document and respond to an effective cyberattack. This information may include the indicators and artifacts gathered before, during and after a successful cyberattack or breach. This data helps security teams uncover the tactics, techniques and procedures (TTPs) the attackers used, and aids in conducting forensic analysis. Incident data is essential for repairing the damage from cyberattacks and making the changes required to prevent similar attacks in the future.

Access and identity data

Monitoring and recording access and identity data helps organizations detect and address privilege creep and privilege misuse or abuse. To uncover insider threats, it’s essential to deploy cybersecurity applications that analyze audit logs which track authentication events, access control changes and changes to user accounts. 

Third-party data

Datasets from in-house sources are often insufficient for mitigating threats. Many organizations supplement their own data with dynamically updated data feeds from third-party commercial or government sources. This lets them access essential information about emerging cyber threats, malware and other vulnerabilities. Combining these feeds with internal data such as logs, network traffic data and user authentication data helps businesses better understand a threat actor’s motives, potential targets and behaviors — allowing them to mount faster, more effective responses to threats.

The Role of the Security Data Lake

Organizations generate enormous amounts of security data across their digital infrastructure. When security teams capture and analyze this data, they’re better positioned to assess and respond to potential threats. A security data lake serves as a central repository for security and contextual data sources, making this information available for immediate use and long-term storage. 

A security data lake solution offers several key advantages over traditional approaches. First, it is a cost-effective means for storing data for longer periods of time. This is particularly important for cybersecurity activities such as threat hunting and behavioral and predictive analytics, all of which rely on large volumes of historical data. 

Built atop a cloud data platform such as the Snowflake Data Cloud, security data lakes eliminate resource contention issues, allowing resource-intensive activities such as threat hunting and incident investigations to proceed without impacting overall performance. 

Having a security data lake also facilitates collaboration with other professionals outside the cybersecurity space, providing security teams with access to the expertise needed to conduct their work more effectively. Examples of these partnerships include engaging security analysts as subject matter experts and working directly with data scientists on behavior models and machine learning analytics projects.

Build Your Security Program on Snowflake

Modern organizations need a flexible and scalable platform to support their security analytics and operations. With Snowflake, organizations can efficiently collect, store and analyze huge volumes of security and contextual data, providing security teams with access to unified data, near-unlimited visibility and powerful analytics. And with Snowflake, you can quickly deploy your cybersecurity applications in your account for off-the-shelf integrations, security content and pre-built interfaces —all without moving your data.  Snowflake enables fast queries and analyses and the option to plug in all types of apps to the security data lake. This makes it easy to consolidate security data from different apps, transform the data and then perform analysis in a single place. 

Read “The Next Generation of Cybersecurity Applications” to discover security technology leaders across five categories that are delivering next-generation applications and data sharing capabilities. Security teams who want to deploy a security data lake strategy to achieve data-driven and cost-effective results at scale can use this report as an ecosystem guide.