How an organization plans for cyberattacks — along with the data it chooses to collect and analyze — play pivotal roles in determining the ultimate impact of a given attack.
That’s why IT executives should at minimum consider investing in a platform that’s best able to help them collect, leverage and understand their enterprise’s data, according to a new report, “The Essential Guide to Security,” from Splunk.
The report details how modern platform solutions, capable of operating as a “security nerve center,” are better suited than many specialized tools at identifying gaps in network defenses.
A security nerve center connects data from the entire security technology stack. By assembling “data-to-everything” on a single platform, agency security teams are better equipped to detect, investigate and take rapid coordinated action against threats, says the report.
“When security teams invest in their security infrastructure, their security ecosystem and skills become stronger, making it possible to expand security practices into new areas and proactively deal with threats,” say the report’s authors.
A data-to-everything platform fosters collaboration between multiple cybersecurity areas, as well as others outside of security so that enterprises can make informed decisions and take appropriate actions.
Security teams can use this modern platform to drive statistical, visual, behavioral and exploratory analytics that inform decisions all the way to invoking actions to address cyberthreats and challenges, the report suggests.
Steps to achieve analytics-driven security
To be effective, a cybersecurity program must continually evolve along with modern threats. The challenge is when leaders lack a clear sense of how to improve security practices.
Knowing where the organization is in its journey will help leaders manage the time and resources of security teams more effectively. The report breaks down six stages to achieving analytics-driven security practices, including:
- Stage 1: Collection — basic security logs and other machine data.
- Stage 2: Normalization — standard security taxonomy and asset and identity data.
- Stage 3: Expansion — additional data sources like endpoint and network activity.
- Stage 4: Enrichment — augment security data with intelligence sources.
- Stage 5: Automation and orchestration — consistent and repeatable SecOps capability.
- Stage 6: Advanced detection — sophisticated detection mechanisms including machine learning.
The report concludes with examples to solve common security challenges. Each example explains the challenge and provides information about data sources, use cases, security solutions, programming difficulty, how to implement, alert volume, known false positives and how best to respond.
Learn more about how to improve the security posture of your organization using an analytics-driven approach.
This article was produced by FedScoop for, and sponsored by, Splunk.