Intro

In cybersecurity, speed, clarity, and cost control are everything—and AI has the potential to deliver all three. But only if it’s done right.

At Observo AI, we use machine learning to eliminate low-value data, reduce alert fatigue, and surface the insights that matter most—all while cutting data volume and storage costs by up to 80%. But for many teams, one critical question still lingers: Can we trust what AI is doing with our data?

We’ve heard this concern from CISOs, SOC leaders, and data engineers alike. That’s why Observo AI is built with humans squarely behind the steering wheel. No black-box models. No silent rewrites of your telemetry. Instead, our platform puts you in control, with complete visibility into every action AI recommends.

You see the "before and after" of each transformation. You can question the AI in natural language—asking why a change was proposed, what patterns were detected, and whether the data supports your specific goals. If the AI recommends dropping 60% of noisy DNS logs, you can inspect the impact, review the logic, and decide whether to accept, adjust, or decline the change.

In this article, we’ll show how Observo AI strikes the right balance between machine intelligence and human judgment—helping your team move faster, stay focused, and always stay in control.

Why Trust in AI Matters for Security Data

Every decision is only as good as the data it’s based on. If an AI engine silently drops telemetry, rewrites fields, or filters out anomalies without transparency, it introduces serious risks. Imagine an AI model mistakenly filtering out 20% of logs from your cloud firewall—not because they’re low-value, but because a model was poorly trained. That could leave real attack activity completely invisible to your SOC. In security operations, false negatives (missed detections) are often much more dangerous than false positives. Trust in AI isn't just about feeling comfortable with automation—it's about knowing that every critical event is still being captured, analyzed, and made available for action.

In October 2023, genetic testing company 23andMe experienced a significant data breach resulting from credential stuffing attacks. Attackers exploited reused credentials to access approximately 14,000 user accounts, but due to the interconnected nature of the platform's features, the breach expanded, exposing sensitive genetic and personal data of about 5.5 million users . The breach went undetected initially, emphasizing the importance of AI systems that can identify unusual access patterns and provide clear explanations for their alerts, allowing human oversight to intervene promptly. (Source Miami University of Ohio)

‍Human-in-the-Loop by Design

Security and data engineering teams stay in control

At Observo AI, we believe that artificial intelligence should enhance—not replace—human decision-making. Our platform is built with a human-in-the-loop design that ensures security and data engineering teams retain full control over their pipelines, priorities, and outcomes. While AI plays a critical role in identifying patterns, optimizing telemetry, and recommending improvements, every action ultimately depends on user oversight. This keeps your team in charge while letting automation handle the heavy lifting.

Models don’t make changes—until you approve them

Observo AI’s agentic architecture is proactive but not autonomous in the way that it removes agency from users–it can take action, but not without your approval. Our models continuously monitor data patterns and pipeline performance, surfacing opportunities to reduce noise, enrich context, or reroute data more efficiently. However, these models don’t make silent or automatic changes. Instead, they generate clear, explainable recommendations—ranked by impact—allowing users to review and act with confidence. 

Users approve, reject, or modify suggestions

Every recommendation from Observo AI is actionable and transparent. Whether it’s identifying redundant log sources, suggesting a data transformation, or flagging a potential anomaly, users have the final say. Security and observability teams can approve a recommendation with a click, adjust its parameters, or reject it entirely. This flexible review loop ensures that AI doesn’t operate in a black box—it operates in partnership with your team.

AI-Native With You in Control

Many security tools treat AI and machine learning as an afterthought—tacked onto legacy platforms in ways that obscure what’s happening behind the curtain. The result? Overridden user intent, ineffective automation, and more confusion than clarity. These bolt-on approaches often leave teams second-guessing the usefulness of AI, rather than trusting it to drive outcomes.

Observo AI is different because we’re AI-native—built from the ground up with machine learning embedded directly into the stream of telemetry data. Our models don’t point you to documentation or add more false alerts. They actively reduce noise, highlight actionable insights, prioritize critical alerts, and detect anomalies in real time—so your team never misses what matters most.

With natural language search and data insights, teams can investigate telemetry the same way they think—no need to master complex query languages or write regex filters. Observo AI gives users the ability to interrogate recommendations and understand what’s happening, what’s important, and what can be safely archived. You always know why a change is suggested, and you can approve, adjust, or decline it with full transparency and control.

Observo AI helps teams move faster and get better results—because our AI works for them, not around them. AI isn’t in our name because it’s trendy. It’s there because it’s core to how we deliver intelligent, explainable data optimization for Security and DevOps. That’s what being AI-native really means: machine learning that automates real-world data engineering tasks and empowers your team—not a marketing gimmick or a flashy overlay that tells experts what they already know.

Training on Company-Specific Data

Out-of-the-box transforms on data source types set the foundation

Observo AI starts with deep, foundational knowledge of common telemetry sources—VPC flow logs, GCP audit logs, application traces, and more. Our machine learning models are pre-trained on a broad set of log types and schemas, enabling immediate transformation, parsing, and enrichment for many standard sources. This gives every customer a powerful head start, allowing them to quickly normalize and route incoming data without building regex filters or maintaining fragile ingestion rules.

Fine-tuned on customer-specific telemetry and context

While global patterns help us recognize the structure of known data types, the real value comes from tailoring those patterns to each customer’s environment. Observo AI fine-tunes its models on your organization’s telemetry—your schema variations, log formats, and custom fields—so that parsing, enrichment, and reduction strategies are optimized for your infrastructure and operational needs. This customer-specific learning is what enables our platform to reduce noise without losing signal, no matter how complex or unique your data may be.

Continuously learns and improves

Once deployed, Observo AI doesn’t stay static. The system continuously evaluates the structure, behavior, and utility of telemetry flowing through your pipelines. As new data sources are introduced or log formats evolve, the platform adapts—automatically refining transformations and surfacing new opportunities for optimization. This continuous learning ensures performance keeps improving over time without requiring manual reconfiguration or the creation of new filters for every data change.

Every customer’s model is trained on their data and unique to them

We take data privacy and architectural independence seriously. That’s why every Observo AI model is trained exclusively on the data it sees within a customer’s environment. There is no cross-training or pooling of customer data—your data stays in your control, in your data plane. This approach not only preserves data security, but also ensures that the intelligence driving your pipeline is truly optimized for your environment, not someone else’s.

Visualizing the Impact of Recommendations

Before/After/Delta dashboards show exactly what changes

Observo AI makes every recommendation measurable and transparent. With built-in Before/After/Delta dashboards, users can clearly see the impact of each optimization—whether it’s a reduction in volume, enrichment of data, or masking PII. This visibility builds trust in the system, allowing teams to confidently approve changes and demonstrate the results to stakeholders.

Monitor data changes over time

Data environments are constantly evolving, and so is the telemetry flowing through them. Observo AI provides time-series views that help teams track how data volume, format, and enrichment are changing across days, weeks, or months. This makes it easy to correlate pipeline changes with improvements in performance, cost savings, or incident response efficiency.

See data reductions at each data source and each pipeline

Observo AI provides granular visibility into where optimizations are happening. Users can drill down to see data reduction metrics by source type, ingestion point, or pipeline stage—enabling precise tuning and clear attribution of results. Whether it’s a noisy application log or an overly verbose cloud audit stream, teams can pinpoint and prove where volume reductions are occurring.

Explore data composition to drive better decisions

Our Data Insights tab gives teams a detailed view into the structure and content of telemetry within any pipeline. You can break down the composition of data—keys, cardinality, percentiles, top 10 values, etc.—so you can better understand your environment, surface optimization opportunities, and improve both performance and security posture. It’s a powerful tool for turning raw data into operational insight.

Interrogating the AI with Natural Language

Ask why a suggestion was made

With Observo Orion, users can go beyond blindly accepting AI recommendations—they can ask why. Every recommendation comes with a clear rationale, and users can interrogate Orion in plain language: Why was this data flagged for reduction? What pattern did you detect? This transparency builds confidence in the system and ensures users understand the logic behind each proposed optimization before taking action.

Ask if that information is useful

Sometimes data isn’t just noisy—it’s contextually irrelevant. Orion helps you assess the utility of log data in relation to your goals. You can ask: Is this pattern useful? What percentage of times does this pattern cause an error? By analyzing signal quality, volume trends, and usage patterns, Orion guides users toward smarter data retention and routing decisions—always keeping human intent at the center.

Explore transformations before deploying

Orion doesn't just recommend transformations—it builds them. Using natural language prompts, users can ask Orion to generate a regex-based filter to exclude redundant or low-value logs or any other reason on a commonly identified pattern. These filters are editable, human-readable, and never deployed automatically. Instead, users can review, test, and refine them directly in the interface. This pairing of automation and human validation ensures both speed and precision—delivering faster outcomes without compromising control.

Conclusion: AI You Can Trust Because You’re in Charge

At Observo AI, we believe AI should amplify human expertise—not replace it. Our platform is built around the principle that the best outcomes come when humans remain at the center of decision-making, supported by intelligent automation that enhances speed, clarity, and confidence.

AI is exceptional at surfacing patterns across massive datasets—patterns that would take humans days or weeks to detect–if ever. But it’s the people behind the screen who decide what matters, what actions to take, and what tradeoffs to make. That’s why Observo AI was designed to keep your team in full control at every step, from analyzing recommendations to deploying filters and optimizing pipeline performance.

With Observo, you get the speed and precision of agentic AI—without the black box. Every insight is explainable. Every action is auditable. And every decision ultimately rests with your team. This is AI you can trust because you’re still in charge.

Want to dive deeper into how AI and human judgment can work together to create more secure, efficient, and cost-effective operations?

‍Download our CISO Field Guide to AI Security Data Pipelines to learn how modern security leaders are putting these principles into action.

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