What is security analytics?

As organizations scale to modern multicloud environments, they’re met with increased complexity that makes managing security far more difficult. Vulnerabilities can enter the software development lifecycle (SDLC) at any stage and can have significant impact if left undetected. As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency.

According to recent global research, CISOs’ security concerns are multiplying. Two-thirds say vulnerability management is becoming harder because of complex supply chain and cloud ecosystems. 75% say team silos and point solutions make it easier for vulnerabilities to slip through to production. Finally, just 50% are confident their applications have been tested for vulnerabilities before going into production.

The result is a landscape where an overwhelming majority of CISOs are concerned that if they can’t find a way to make DevSecOps work more effectively, vulnerabilities will be harder to identify, contain, and eliminate.

Fortunately, CISOs can use security analytics to improve visibility of complex environments and enable proactive protection. Here’s how.

What is security analytics?

Security analytics combines data collection, aggregation, and analysis to search for and identify potential threats. Using a combination of historical data and information collected in real time, security teams can detect threats earlier in the SDLC. They can also develop proactive security measures capable of stopping threats before they breach network defenses.

For example, an organization might use security analytics tools to monitor user behavior and network traffic. If they detect users logging on at odd times or in odd locations — such as outside of work hours or from a geographically distant location — and see a corresponding spike in network traffic, security analytics can provide data about potential indicators of compromise (IOC). Teams can then act before attackers have the chance to compromise key data or bring down critical systems.

Why is security analytics important?

A security analytics platform offers several benefits for organizations, including the following:

Early threat detection

Security analytics tools use historical and current data to evaluate threat landscapes and predict likely outcomes. Early threat detection allows for timely remediation, preventing potential breaches and disruptions. This enables security teams to focus their efforts on likely threats and their telltale IOCs, helping to quickly remediate and prepare a plan of action going forward.

Improved compliance

A better understanding of data security across multiple applications and environments provides a unified view of events and information. This offers two advantages for compliance. First, managers can quickly track and respond to threats. Second, companies have an auditable chain of data used to demonstrate security due diligence.

Enhanced insights

A security analytics platform can help provide end-to-end attack analysis using techniques such as log forensics. This data helps teams see where attacks began, which systems were targeted, and what techniques attackers used. Equipped with improved insights, organizations can build out cybersecurity policies that address common concerns.

Increased visibility

Security analytics can help make connections between multiple applications, services, and clouds. This means they provide increased visibility for organizations across increasingly disparate IT environments. This visibility makes it possible for teams to better understand where protection is sufficient, where it needs work, and where it needs immediate action.

Proactive protection

Reactive defense can help limit an attack’s impact but can’t eliminate the attack before it begins. Proactive protection, however, focuses on finding evidence of attacks before they compromise key systems. A proactive approach allows teams to turn the tables on attackers by making the first strike.

Security analytics vs. SIEM

Security information and event management (SIEM) tools are staples of enterprise security. These solutions offer a way to protect perimeter-based networks and pinpoint potential attacks using a signature-based approach.

Meanwhile, security analytics tools leverage behavior-based analysis to continuously monitor cloud, on-prem, and hybrid networks.

Here’s a look at the main features of SIEM and security analytics tools and how they stack up.

Common application

Organizations typically use SIEM tools to monitor monolithic applications and evaluate long development and release cycles. Security analytics solutions are designed to handle modern applications that rely on dynamic code and microservices.

Infrastructure type

In most cases, legacy SIEM tools are on-premises. Security analytics tools can be any combination of on-prem, in the cloud, or both.

Deployment time

Deploying SIEM tools — especially in established environments — can take months. Cloud-based security analytics tools, meanwhile, can be set up and running within days or even hours.

Security approach

SIEM tools use a signature-based approach to detect threats. If the code doesn’t carry a known signature, it may gain access even if it contains malicious payloads. Security analytics uses machine learning to ingest data and identify potential attack signatures, behavior patterns, and trends.

Potential visibility

Security analytics helps organizations gain a holistic view of their IT environments, including application programming interfaces and legacy solutions. SIEM confines security to narrow bands of visibility tied to specific attack signatures.

Put simply, SIEM tools excel at protecting the known by addressing and eliminating previously detected security threats. Security analytics platforms, by contrast, protect the unknown. Instead of focusing on the specific signature of attacks, security analytics tools target the underlying behavior, enabling organizations to detect threats earlier and respond ASAP.

Challenges of effective security analytics

While security analytics is a step up from legacy SIEM tools, it does not come without challenges.

The first challenge of effective security analytics involves distributed data sources. The nature of “anytime, anywhere” data generation means data is no longer confined to structured processes and can’t always be defined by existing policies. While bigger data pools mean more access to potential insights, they come with the challenge of visibility. How do companies reliably find, review, and analyze this data?

Security analytics must also contend with the multicomponent architecture of modern IT infrastructure. This includes everything from multicloud deployments to microservices to Kubernetes instances and the use of open source software. The net result is a growing challenge in getting to the root cause. While it’s easy for analytics solutions to pinpoint symptoms, determining the communal source is much more difficult.

Dehydrated data also poses a challenge to effective security analytics. Dehydrated data has been compressed or otherwise altered for storage in a data warehouse. While it still has value once it has been rehydrated into its original form, this process can be time and resource-intensive. Moreover, given the time-sensitive nature of many threats, organizations can’t afford to wait for data to expand back into its original form.

The importance of observability for a security analytics platform

When it comes to security analytics, increased context and insights are the key benefits. Better visibility means more data to draw insights from, providing more comprehensive threat analysis and response.

As a result, observability is a must-have for any security analytics platform. Without observability, teams are missing some (or all) of the context surrounding current and past security events. But, observability doesn’t stop at simply discovering data across your network. To deliver actionable insights, observability must deliver dynamic insights and help provide context for key data sources.

Observability starts with the collection, storage, and accessibility of multiple sources. It also includes ongoing reports from those sources, such as applications and services that are updated once, twice, or multiple times per day.

Finally, observability helps organizations understand the connections between disparate software, hardware, and infrastructure resources. While data about the software in isolation provides some value, it lacks the broader context necessary to address the issue at scale. For example, updating a piece of software might cause a hardware compatibility issue, which translates to an infrastructure challenge.

How a data lakehouse enables security analytics

The Dynatrace Grail lakehouse allows organizations to maximize and enable effective security analytics. A data lakehouse combines the benefits of a data warehouse and a data lake to deliver the best of both worlds. While data warehouses are purpose-built to handle structured data, they’re optimized for specific use cases, which limits their broad applicability. Data lakes, meanwhile, are capable of ingesting and managing both structured and unstructured data, which increases openness and flexibility. However, this typically comes at the cost of data quality, limiting the value of analysis.

Dynatrace Grail introduces a new architectural design that addresses both of these issues to provide both rich data management and low-cost cloud storage.

Grail contains the following three key components to help organizations succeed:

Ingestion and processing

Grail is designed to ingest hundreds of petabytes of data every day. Organizations can choose to ingest data using Dynatrace OneAgent, or open-source options such as OpenTelemetry or Micrometer. But it’s worth noting that OneAgent is the only option that collects data from every tier of your application stack without requiring configuration.

Data retention

Data loaded into Grail is automatically retained for up to three years and is fully flexible based on business needs. Additionally, with the Dynatrace Query Language, data is available in real time.

Data analytics

Using its massively parallel processing engine, Grail can ingest data without indexing, meaning that data isn’t dehydrated. Instead, data can be queried to answer any questions in real time. In addition, schema-on-read allows IT teams to store data in its native format.

Protecting your data with unified observability and security analytics

Broad and deep observability is a key step in ensuring the effectiveness of your organization’s security analytics program. Dynatrace’s platform approach to unified observability and security helps organizations achieve end-to-end insights, protect their applications and services from vulnerabilities, and increase the delivery speed of high-quality, secure software. Bolstered by powerful AI and intelligent automation, Dynatrace can help your organization stay secure, efficient, and scalable.