Operations | Monitoring | ITSM | DevOps | Cloud

As CEO of Observo AI, I've spent countless hours with CISOs and security leaders discussing their most pressing challenges. The conversations always circle back to the same fundamental problem: security teams are drowning in data they can't afford to ignore but can't afford to process. Today's announcement of our strategic partnership with GuidePoint Security represents more than just another vendor alliance.

Splunk is the backbone of security analytics for many of the world’s most advanced enterprises. Its ability to unify, correlate, and analyze massive volumes of telemetry data has made it the platform of choice for SOC teams worldwide. But as security data grows exponentially in volume and complexity, many organizations face a different challenge: getting the right data into Splunk, in the right shape, without straining infrastructure resources or missing critical signals.

Cyber threats are growing more cunning every day, with attackers even tapping into artificial intelligence to outsmart traditional defenses. Organizations face a flood of security data—logs, alerts, and telemetry—making it nearly impossible to sift through. How do you spot the real dangers amid all that noise? Observo AI’s ML-Powered Threat Insights offers a game-changing answer.

As security and DevOps teams grapple with rising telemetry volumes and ballooning tool costs, they’re increasingly forced into tradeoffs: index less, ingest less, search less. That might save money in the short term—but it comes at the cost of impairing long-term visibility, historical investigation, and strategic clarity. Traditional search tools are built around precision. They’re useful when you know what you’re looking for—but far less helpful when you don’t.

When we talk to security leaders, the theme is almost always the same: “How do we keep up with explosive telemetry growth without blowing our budget—or compromising visibility?” That’s exactly what BILL, a leader in financial operations software, was grappling with.

Security and DevOps teams are drowning in data. Fueled by the explosion of cloud-native architectures, microservices, and accelerated software development cycles driven by AI, telemetry volumes are growing faster than ever. For most organizations, security and observability data is now doubling every 2–3 years. At the same time, most of the tools used to analyze that data—SIEMs, log analytics platforms, and cloud-native observability tools—charge based on ingestion volume.

Enterprise observability and security architectures are being crushed by the cost and complexity of collecting telemetry at scale. With thousands of VMs, bare-metal hosts, and containerized services spread across hybrid environments, most teams rely on a fragmented patchwork of Syslog agents, Fluentd/Fluent Bit nodes, Syslog daemons, other open-source agents or collectors, and proprietary vendor forwarders. What starts as a well-intentioned data collection plan quickly turns into a maintenance nightmare.

When security data volumes double every two to three years but budgets stay mostly flat, achieving a fast return on investment is the only way most security organizations can get new technologies approved. Teams can’t afford to wait months to see results—they need solutions that pay off starting on the first day of the proof-of-value period. AI-powered data pipelines make that possible.

For many organizations, the first motivation to modernize their security data infrastructure is cost. And understandably so—data volumes are exploding, and the costs of storing and analyzing everything in a traditional SIEM can quickly become unsustainable. But in my experience, cost savings are just the entry point. The true value of optimizing security data goes much deeper.

‍This is the third and final post in our "Data Intelligence in Security: The AI Pipeline Revolution" series. In Part 1, we explored why AI-powered security data pipelines have become essential for modern SOCs. Part 2 covered the critical capabilities to evaluate when selecting a solution. Today, we'll share implementation best practices and examine the business impact you can expect.