Security 101: Logs, Data, and Modern IT | Sumo Logic
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Security 101

Data, Logs, and Security in a Modern IT Environment
Security padlock on digital data background

Today’s security needs are increasingly complex.

With a new data breach making headlines virtually every day and businesses of all sizes losing millions on compromised data, companies and other organizations can’t overlook cyber security any more.

And while it’s easy enough to commit to enhanced security, it can be tricky—even for IT and other technical teams—to understand just exactly how  to navigate today’s complex security environment.

Here, we’ll look at several important elements of security in cloud-enabled, data-heavy environments and consider how log analysis and machine learning fits into this picture.

What Is Data Security?

Generally, data security includes a mix of technologies, procedures, processes, and policies that protect an organization’s data and networks and mitigates the risk of a breach. Properly functioning programs also minimize the amount of damage that occurs from a data breach through its response to the incident.

As cyber threats have increased in prevalence and sophistication, the ideal security model has evolved into a proactive operation that adapts to changes, stays on top of emerging threats, and monitors activity intelligently, with an eye toward operational priorities. The

Concept of examining data analytics.

Analytics reveal security events in real-time.

essential areas of the modern IT security operation include:

  • Network security
  • Information security and privacy
  • Training programs for end users
  • Incident response and disaster recovery
  • Operational security
  • Application security
  • Cloud security
  • Regulatory compliance

The Challenge of Logs in Security Monitoring

Monitoring data logs is a critical cyber security operation, but it also can quickly become a burden for firms that are underprepared. In fact, for many security teams, the challenge of analyzing terabytes of logs has become downright overwhelming.

As these teams attempt to keep up with data using home-grown tools, their organizations become increasingly vulnerable to malware, external attacks, insider threats, data breaches, advanced persistent threats (APTs), and other security threats.

Retaining, reviewing, and reporting on activity recorded within ever-growing log datasets becomes more difficult and expensive every day, making it nearly impossible to stay compliant with evolving security regulations.

Today’s enterprises require a new approach to log monitoring and management. These solutions must check each of these four boxes:

  1. Easy to adopt and deploy across data centers and the cloud.
  2. Scale to collect, manage, and analyze exponentially more log data.
  3. Able to automatically detect and flag potentially malicious activity.
  4. Enable low-cost retention and easy reporting for compliance.

Making Use of Machine Data

Because machine data contains a complete record of events related to an organization’s security posture, it is a critical part of enforcing security, operational, and regulatory compliance. But again, the growth in machine data has been explosive, and today’s organizations are struggling to keep up. An organization’s failure to turn machine data into actionable insights results in mounting vulnerabilities and escalated risk.

Compliance requires enterprises to be able to easily audit and investigate incidents, taking rapid action to remediate both security and compliance-related issues. That means alerts must be triggered in real-time, compliance reporting must be timely, and data must be retained over the long-term.

Additionally, to effectively enforce security, operational, and regulatory compliance by leveraging machine data, organizations need a platform capable of scaling to handle the volume of data generated and robust enough to transform that data into insights.

Analytics Uses in Security

Machine data analytics involves aggregating, parsing, and visualizing data generated by software from a wide variety of sources.

Machine data analytics is well-suited to supporting security tasks. Proactive and predictive analytics powered by machine learning algorithms uncover unknown security events across your stack, improving threat detection models and helping to reduce risks. Analysts can monitor systems in real-time to identify issues, problems, and attacks before they impact customers, services, and revenue.

SIEM & Threat Management

Bank safe data security illustration.

Protecting data is easier with platform security.

Security information and event management (SIEM) solutions have been around since 2000, and they were developed with the goal of helping organizations in the early detection of targeted attacks and data breaches. Needs for these tools ranged from analyzing event data in real-time to the collection, storage and analysis of log data for incident forensics and regulatory compliance.

While SIEM tools still show promise in cyber security, traditional tools have been unable to keep up with the data production and consumption of information security operations.

The production and consumption of data has ballooned considerably alongside increasingly sophisticated best practices and expanded compliance and regulatory requirements. Event and activity logs have grown to the scale of big data, and the types of data being consumed have become significantly more varied.

As threat intelligence data rolls in to the SIEM, most of it appears to be critical—making effective response an impossible task within a reasonable budget. With so many critical alerts, IT and security have stopped searching for a needle in a haystack and started identifying and prioritizing a needle in a stack of needles.

The big data era is showing information security teams that there’s more that can (and must) be done to identify threats, reduce risk, address fraud, and improve compliance monitoring activities by bringing better context to data and creating information for actionable intelligence.

Platform Security

Platform security is a form of security architecture that is centralized and unified. Unlike layered security architecture, in which each layer or system in the structure is security-managed independently, platform security approaches security on the structure as a whole.

The advantage to a platform approach to security is that it reduces the need for organizations to maintain a diversity of applications and controls for each layer, making for a less-complex and more easily secured structure.

Illustration of a cloud-based data network.

Cloud-enabled tools meet unique security management needs.

Cloud Security

Cloud computing is reshaping not only the technology landscape, but also how companies think about and execute their innovation process and practices to enable faster, differentiated, and personalized customer experiences and services.

However, operating in the cloud securely and confidently requires a new set of rules and a different way of thinking. Security organizations must alter their operational mindsets and processes from traditional data center-centric models to new, more statistical models.

While many veteran security professionals approach the cloud with caution, the cloud is here to stay. On the upside, the cloud provides an extremely powerful new set of tools for securing a cloud environment.

In a system designed for the cloud, organizations have the tools to design, implement, and refine policies, controls, and enforcement in a streamlined and centralized fashion.

Deploying services in the cloud gives firms the freedom to design networks and security measures from the ground up while implementing secure designs in code, so you’re not subject to the same concerns you have in a physical data center or hosting facility.

Cloud tools also make it possible to take security management to a new level by enabling full automation of controls and tests. Security teams are free to design systems with all the security controls they could ever want but previously couldn’t achieve.

AWS Security

As organizations transition application workloads to Amazon Web Services (AWS), it’s critical that they monitor the security of those services. Organizations need continuous intelligence about their cloud infrastructure in the form of real-time machine data analytics that generate operational, security, and business insights.

AWS offers cloud computing resources that have become vital for more than a million organizations. These enterprises all need to define their own unique set of security requirements for their AWS environment. And with that kind of complexity in security requirements, there’s simply no way for Amazon, as a single vendor, to offer security protection that is adequate for all of their customers.

To meet their security needs, AWS customers often must look to third-party solutions to suit their security needs.

As AWS has made itself pervasive across industries, many vendors have stepped in to cover the security gap. Those solutions target the following specializations:

  • Network firewalls
  • Endpoint security
  • Configuration assessment
  • Identity and access management
  • Log analytics

Microsoft Azure Security

Like other cloud computing services, Microsoft’s Azure cloud platform has it’s own security and monitoring challenges.

For effective security monitoring, new Azure applications architectures require new monitoring approaches. Companies need proactive security monitoring to identify known and unknown threats (without creating rules or schemas) using machine learning algorithms; and to simplify compliance run audits and facilitate central logging to meet PCI, HIPAA, FISMA, GLBA, and COBit.

DevOps and Security

In traditional software development environments, security has always been considered a separate aspect—even an afterthought—but now the two practices have emerged to produce safer software in the form of Rugged DevOps and DevSecOps.

Rugged DevOps is an emerging trend that emphasizes a security-first approach to every phase of software development. DevSecOps combines traditional DevOps approaches with more a more integrated and robust approach to security. These approaches are not mutually exclusive, and take slightly different paths toward the same goal of shifting security leftward and continually focusing on it through the production pipeline.

As today’s environments evolve toward continuous delivery models that can see multiple production releases per day, any miscalculation or error in security can clog up the production pipeline. But Rugged DevOps and DevSecOps approaches can help organizations achieve state-of-the-art design security.

Enterprise-Level Data Security Concerns

More than 90% of breaches go undetected by corporations, so it’s clear they need a new approach to counter today’s cyber attacks. This approach should be based on speed of detection, the ability to proactively root out potential security issues before they impact the organization, and scale to meet current and future data volumes.
Critical requirements to handle enterprise security analytics include:

  • The ability to analyze data generated across an entire environment, including custom applications, networks, security devices, operating systems, and more.
  • Out-of-the-box visualizations and content for specific machine data sources that help with forensic and trending analysis.

The ability to uncover data anomalies that may indicate a cyber attack or compliance violation, without the need to write rules.With today’s attacks originating from both outside and inside the firewall, legacy tools rely on heavyweight and reactive mechanisms to identify threats and lack the predictive analytics that today’s enterprises require. Instead, enterprise-level companies need enterprise-grade security solutions.

  • Identify data exfiltration by uncovering and correlating security events across multiple data sources.
  • Reduce compliance costs by accelerating and simplifying compliance reporting and auditing, as well as providing continuous compliance management.
  • Audit access to sensitive and mission-critical applications that are both on-premises and in the cloud.

The Intersection of Security and Compliance

Maintaining regulatory compliance is more challenging than ever. PCI DSS, HIPAA, SOC II, FISMA, and other regulations require log data retention, routine reviews, and reporting on specific activity within your infrastructure. To comply, not only must you securely retain an ever-larger volume of activity logs, but you must adapt to evolving regulation.

You also must satisfy individual external auditors with their own subjective views of compliance reporting. For them, canned reports simply won’t do.

All of this requires a system that is flexible, scalable, and enables you to adapt to individual regulations and auditors.

Learn more about how the Sumo Logic platform can support your security and compliance needs.

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“Sumo Logic brings everything together into one interface where we can quickly scan across 1,000 servers and gigabytes of logs and quickly identify problems. It’s awesome software and awesome support.”

Jon Dokuli,
VP of Engineering

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