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Understanding artificial intelligence for log analytics

Artificial intelligence (AI), machine intelligence, machine learning, and deep learning are terms often used interchangeably but have distinct differences. AI is a broad concept referring to machines that can perform tasks in ways similar to human intelligence. AI and machine intelligence are the same. However, under the AI umbrella lies machine learning, which involves machines automatically learning and improving from data. Within machine learning is a subset called deep learning, which uses artificial neural networks (ANN) to process vast amounts of data in layered structures. 

Deep learning has been instrumental in advancements such as self-driving cars and advanced image recognition. Notably, deep learning models can automate the feature engineering process, distinguishing them from traditional machine learning models. The increasing emphasis on deep learning within AI research is reshaping the technological landscape, showcasing its potential in diverse applications. The latest application to Sumo Logic is the relevance of AIOps, where artificial intelligence applies to IT operations.

Artificial intelligence vs machine learning

Machine intelligence for log analytics 

Machine intelligence for log analytics applies to various applications where access, usage and performance information can be used to draw insights. A security team, for example, might use analytics to detect real-time threats by pinpointing network access anomalies, allowing for quick action toward resolution.

As another example, machine data captured from customer-facing sites and applications can give teams insights into customers’ interactions across multiple channels. Infrastructure teams, for instance, can track where customers are accessing resources, when user access is spiking or lagging, and how systems perform at high-volume periods. This information can then be used to make improvements to improve the user’s experience and build revenue.

Leveraging machine data has many use cases in a security context as well. Sumo Logic currently employs machine learning capabilities in various security products, which allows security teams to operate efficiently at scale without the constant deluge of alerts, tricky classification and triaging scenarios. The Sumo Logic Global Intelligence Service (GIS) provides real-time and actionable insights about industry technology adoption trends. 

In addition to using machine learning as part of GIS, Sumo Logic also employs machine data analytics for its Cloud SIEM Insights Trainer. Using customers’ individually siloed data sets, the Insight Trainer offers suggestions on rule tuning within Cloud SIEM to reduce the amount of false positives. This keeps the security analyst on the right track, investigating real threats and not wasting valuable time chasing down incorrect indicators of compromise (IOCs). 

Why is machine data important?

As more and more businesses become increasingly software-centric, harnessing machine data's power to drive intelligence and speed is paramount. This new world can be painful and difficult for translating insights into positive business outcomes.

Contemporary businesses across numerous industries require continuous insights into their systems’ operations. At any given moment, these businesses need to be able to answer several essential questions:

  • How are our systems performing? Are there any bottlenecks? Will we need to scale up to handle increasing volume?
  • Who is trying to access my systems? Where are they entering from? Who gets in, and what do they do once logged in?
  • Are there anomalies to address? Is there anything out of the ordinary?

Without machine data analytics, data isn’t consolidated, so diagnosing issues means manually checking logs across multiple systems.

Continuous innovation means companies are constantly building, running, and securing modern applications and delivering new, personalized services to customers faster than ever before. This new approach also depends on cloud infrastructures and agile DevOps models for greater speed, agility, and scalability. The success of these new modern applications also hinges on leveraging machine data analytics capabilities.

What is machine learning in a big data context?

While data analytics helps professionals find insights from machine data, machine learning goes further. Using algorithms for iterative learning, machine learning enables computers to generate insights from the machine data they take in.

In other words, it automates the process of machine data analytics.

With machine learning, you can parse and organize large volumes of unstructured and semi-structured machine data from complex, modern applications.

  • Prioritize application development efforts based on your users’ behavioral patterns, including seasonality and cyclicality.
  • Rapidly surface operational issues based on multi-dimensional comparisons, pattern extraction, and anomaly detection.
  • Quickly identify threats that indicate elevated security risk and prioritize these threats without creating predefined policies or rules.

Machine learning can be beneficial for identifying anomalous activity that varies from recognized patterns. It also can help teams cut through the noise of overwhelming data and find areas where they can focus analytics efforts.

Practical uses of log analytics and machine learning

Within a single organization, different teams will have various applications for machine data analytics.

IT: Teams use machine data analytics to monitor and scale resources, troubleshoot performance and access issues, and minimize downtime, among other technical applications.

DevOps: Log and machine data analytics services that use predictive algorithms, and can be seamlessly integrated with a host of DevOps tools, can improve continuous integration and continuous deployment processes. Analytics tools enable teams to understand error rates, failures, and other information in massive amounts of log and machine data.

Cloud: Machine data analytics from AWS apps and other public cloud sources can help companies accelerate the building, running, and securing of modern cloud applications to achieve greater visibility into their workloads. Additionally, correlating data from multiple data sources and showing it in the context of time-series metrics provides a common source of truth for monitoring and troubleshooting.

Security: Machine data analytics is well-suited to security tasks. Advanced analytics and machine learning can help improve threat detection models. Analysts can monitor systems in real-time to identify issues, problems, and attacks before they impact customers, services, and revenue.

Compliance: Machine data analytics can help organizations quickly and easily demonstrate that they retain all relevant activity logs and perform routine analysis for industry-mandated compliance regulations like HIPAA or PCI DSS.

Challenges with machine data analytics

The biggest problem with harnessing machine data is the sheer volume of data being generated. Raw machine data contains billions, if not trillions, of log and metric data points and is increasing in quantity at an exponential rate. The volume and velocity of this data growth can be difficult for single-tenant analytics solutions to handle.

Additionally, machine data can come in a variety of formats and can be structured, unstructured, or semi-structured. Only some solutions on the market are equipped to handle the variety of data formats efficiently. To make the most out of a machine data analytics platform, customers should look for providers who offer a data lake to streamline and centralize ingestion across log sources and formats.

It is best to be cautious with machine learning technologies, especially if a vendor offers any form of AI with zero insight into what the learning model is. Compliance when handling sensitive data is paramount in highly-regulated industries (think hospitals, finance, etc.). It could violate regulatory frameworks if a vendor deploys a machine learning technology that does not correctly obfuscate or anonymize sensitive data. If the personally identifiable information (PII) is left vulnerable due to a lack of proper data compliance, end users are vulnerable to attack. In order for IT teams to properly manage, disclose or release end user PII data, a human in the loop is always required for administering access control.

Be aware and ask questions about how your industry's data is handled and how it is fed into a given machine learning model. Further safeguards can be placed around your organization's data to ensure that only qualified team members or departments can access relevant sensitive data. Role-based access controls (RBACs) are highly effective at stratifying which users specifically can access what data. Supporting technologies such as RBACs is vital to good security hygiene for organizations of every size.

The future of machine intelligence: predictive analytics and generative AI

Generative AI is a branch of artificial intelligence that mimics patterns from mathematical models and to create new content, text, images, or recommendations. A commonly used example these days comes in the form of natural language processing (NLP) models like OpenAI’s ChatGPT. In the world of AIOps, however, it can show up in the form of predictive analytics, which anticipates future trends based on past data. Generative AI can use this forecasted information to automatically create simulations from known incident response playbooks and attack vectors. This allows practitioners to create strategies tailored for these predictive models. The fusion of these technologies will redefine how businesses tackle future challenges.

With the digital landscape rapidly evolving for data analytics, Sumo Logic is entering at the bleeding edge of predictive analytics. Sumo Logic recently unveiled its “Predict for Metrics” feature, which analyzes historical data using linear and autoregressive machine analytics models for logs and metrics. Businesses can now use this feature to manage resources and anticipate future application needs proactively.

From a monitoring and troubleshooting perspective we know monitors trigger multiple times per day, which can lead to alert fatigue, frustrated engineers and hinders innovation. Sumo Logic’s AI-driven alerting and anomaly detection capabilities, you can cut that down to 60-90% fewer alerts from such noisy monitors. Monitors can also trigger one or more playbooks to drive auto-diagnosis or remediation, and accelerate time to recovery for application incidents. Faster issue remediation means engineers can focus more time on important projects.

Additionally, many security vendors will start to offer cloud technologies such as cloud security posture management (CSPM), which provides a more proactive approach to monitoring and securing cloud environments. By combining the core elements of CSPM with technologies such as generative AI, vendors can offer solutions that identify possible cloud infrastructure vulnerabilities and provide AI-generated recommended remediation steps using natural language. Emerging technologies such as CSPM leveraging AI and machine learning can pinpoint problems such as misconfigurations or applications that may be out of compliance for users without manually monitoring and triaging the hundreds of microservices necessary for modern-day cloud-native operations.

The intersection of predictive analytics and generative AI promises to be the next frontier in the digital revolution. As we navigate this evolving landscape, it's crucial to stay updated, embrace these cutting-edge developments, and envision their transformative potential.

A modern solution for modern apps

“Sumo Logic makes it much easier to find and then correct issues in our distributed applications. This lets our developers concentrate on creating new software rather than wasting time manually wading through logs.”

Brett Nelson

SaaS Platform Architect at Pitney Bowes