The security stack is a crucial part of any company’s IT infrastructure. However, Security teams increasingly report that traditional SIEM solution approaches are “costly, complex, and resource-consuming,” according to a recent ESG survey. Fortunately, there has been significant innovation in how firms approach cybersecurity with new cloud-native technologies stack and breaking free from vendor lock-in and giving themselves more flexibility, cost advantage, and future-proofing.

Observability has gained a lot of momentum in the past year, be it full stack observability or data observability. Modern complex IT systems using clouds, microservices and serverless are easy to develop and deploy but extremely difficult to observe. These systems generate tremendous amounts of data and need an automated way of handling the volume. The next era of delivering customer experience is underpinned by the full stack observability capability.

“Have you tried shutting it off and turning it back on?” While AIOps won’t likely remove this query from our vocabulary any time soon, technology is certainly here to take on a bulk of the heavy lifting. For all-sized companies, service calls are still going to continue to pour in. And, there’s no sign of any of the world’s CompTIA certs going to waste in the near future. Still, thanks to AIOps, many jobs within the world of IT will become more streamlined.

CloudFabrix solution provides a holistic approach for enterprises to implement proactive operations with the objective of eliminating/reducing critical incidents and improving customer satisfaction. The solution primarily relies on applying regression/forecasting models on any time-series data to detect and forecast anomalies. One of the unique features of the solution is the ability to convert unstructured data such as logs/incidents/alerts into time-series data to be used for running prediction models.

This blog post covers 4 data masking techniques and data obfuscation techniques that you can implement with Robotic Data Automation (RDA) to mask or hide sensitive data or personally identifiable information (PII) like social security numbers (SSNs) that may have crept unintentionally in logs or events.

In this highly dynamic environment, organizations are looking for ways to innovate and manage resources efficiently. In the first part of the two-part blog series, we saw how organizations handled incidents without an AIOps solution and how long it took to resolve that incident — a scenario representing different steps to resolve an incident. In the second part of the two-part blog series, we look at how organizations were able to handle incidents after deploying AIOps.

As per Gartner, Hyper Automation is the top strategic technology trend for Enterprises. “The shift towards hyper automation will be a key factor enabling enterprises to achieve operational excellence, and subsequently cost savings, in a digital-first world,” said Cathy Tornbohm, Distinguished Research Vice President at Gartner. Businesses want to enable employees to make better decisions in the most cost effective way.

Organizations are always looking for new ways to innovate and reduce costs and allocate resources more efficiently. In this blog post, we will look at how enterprises handled incidents before and after deploying AIOps.

People’s involvement has been inevitable with log management despite advancements in ITOps. Log management at a high level collects and indexes all your application and system log files so that you can search through them quickly. It also lets you define rules based on log patterns so that you can get alerts when an anomaly occurs. Log management analytics solution leveraging RDA has been able to detect anomalies and aid predictive models over a machine learning layer.