As production settings grow more dispersed and ephemeral, SecOps and DevOps teams struggle to comprehend system availability and performance. Despite the proliferation of monitoring solutions available today, real-time visibility remains elusive. Software, now in the form of distributed, high-velocity, and complicated cloud infrastructures continues to be the only known method to build a contemporary organization efficiently.
Security analysts have two challenges: too much data and not enough data. According to a recent Ponemon Institute poll, information overload is a significant source of workplace stress for 71% of respondents. Additionally, 63% of the respondents cited a lack of visibility into the infrastructure and network as a critical challenge they’d like to overcome.
Issues like irresponsible insiders and dispersed DoS assaults complicate today’s SOC environment. And cloud-native apps running on containers and other transitory technology add to the problems. Modern applications and infrastructure are more transitory and complicated than ever before, and conventional monitoring hasn’t kept up.
Challenges that arise with disparity between Development and Security
- App developers aim to deploy code rapidly while maintaining a high level of quality, which means a low bug count. SREs, on the other hand, are motivated by uptime, performance, and efficiency. It’s all about risk reduction and breach mitigation for SecOps.
- When changes aren’t communicated effectively, problems arise. SREs and SecOps, for example, rarely have insight into what developers have changed.
- Fresh code may either merely make modest modifications that don’t affect current activities or replace significant functionality sections across the codebase, including calls to external and third-party programs.
- DevOps strives for rapid deployment. Deployments are slowed by waiting for clearance from other teams. As a result, thorough reviews to iron out bugs are not carried out.
- Dealing with information asymmetry has spawned a slew of new collaborative approaches, beginning with DevOps and progressing via DevSecOps and other variants. The problem is that one party, the developers, has access to more data than the others. Unbalanced risk-sharing results from this knowledge imbalance
- True teamwork has been proven difficult with such differences. Early DevOps initiatives are often effective, but expanding beyond five to seven teams is problematic due to a lack of IT operations knowledge or SRE capability to staff several product teams. Furthermore, the change velocity that DevOps teams can accomplish is often significantly faster than what SREs and SecOps can absorb, resulting in increased information imbalance.
An approach must be devised if teams are unable to sustain high levels of cooperation and communication.
The power of Observability has the potential to bring disparate groups together. Unlike more rigid monitoring systems, an observable system enables users to ask open-ended questions about their data. Pervasive instrumentation across applications, infrastructure, and third-party software is required to meet IT executives’ observability requirements. SREs and SecOps teams may examine apps about their behavior using observability approaches such as collecting all events, metrics, traces, and logs
Beyond conventional methodologies
Only one dimension of complexity is driving observability. Beyond conventional monitoring’s flaws, observability is becoming more vital as security operations teams collaborate across departments. SOC teams now work alongside infrastructure, operations, and DevOps teams, each with its own set of tools and analytics platforms. This kind of contact is something that security operations teams may not have done before. It causes tension between these teams as they try to figure out what different data sets signify or what the right solution looks like. By sending the appropriate data to the appropriate platforms, Observability aids in the resolution of various data pipelines.
Operations teams have begun moving from static monitoring to dynamic observability in recent months. While monitoring focuses on component health, observability gives fine-grained system visibility. Businesses must establish observable systems by embedding metrics, logs, and traces into infrastructure and applications. Data from network traffic, changelogs, and IT service management may help businesses understand the larger picture. Observability solutions may use social media feeds to detect consumer app issues before they reach metrics-based dashboards.
The Importance of Observability
Observability is driven by a variety of factors, including complexity. It’s not simply the limitations of conventional monitoring that drive observability; it’s also becoming more important as security operations teams collaborate across departments. The security operations center now collaborates with infrastructure, operations, and DevOps teams, each with its own analytics and tools platforms. This is something that security operations teams may not have done before, and it may cause friction between them, such as disagreements over what constitutes a proper decision or what the different data pipelines signify. By supplying the appropriate data to the appropriate platforms, Observability may help to address these problems.
The Pipeline of Observability
It’s critical for organizations to control their data and avoid having it tied up in a single vendor system. It’s also critical that data be accessible to everyone in the business. Delivering data to the correct platforms becomes difficult with instrumented systems. A detached strategy may be useful in resolving the problem.
Businesses can route data more effectively and efficiently between distributed and disparate data sources and target systems with observability pipelines, making it simple to consume observability data. Companies don’t have to worry about deciding what data to transfer, where to send it, or how to deliver it. The pipeline receives all of the data and filters and distributes it to the appropriate locations. It also functions as a buffer between data producers and consumers, allowing for more flexibility in eliminating or adding data sinks.
How to Implement Observability Pipeline?
Delivering data to the correct platforms becomes complex with instrumented systems since it decouples inputs, such as applications and infrastructure, from destinations, such as log analytics and SIEM platforms.
Most companies have ten or more security and analytics technologies, and almost half say they need more. Teams can offer more flexible data if they abstract data analysis and usage from how it was acquired. It also enables fine-grained data source optimization, such as redaction, filtering, and data volume reduction.
After observable instrumentation and the pipeline, the final component is data exploration. You can easily compare conventional monitoring to data warehousing. You know what data you’re consuming and the reports or dashboards you’re making in both data warehousing and monitoring. You have a set of well-known questions to ask about well-known facts making it dependable and well-understood.
The concept of observability is similar to that of a data lake. You do not really know what queries you’ll have with a data lake, so you load it with data and arrange it to prepare for them. A data lake is for unknown questions over unknown data, similar to how a data warehouse is for known questions over known data. Because you’re constructing the questions you want to ask while examining the data, it’s frequently beneficial to think of a data lake as a question creation environment. Unlike a traditional data lake, an observability data lake that supports data scientists and optimizes SQL and Python for search.
Security analysts have far too much data to handle and evaluate, yet they still don’t have all of the information they need to see their environment. Traditional tactics such as monitoring may have handled certain issues in the past. Still, they are soon becoming obsolete due to advancements in the IT environment, such as cloud-native or container-based architecture.
When it comes to dealing with complexity, teams need to take a different approach. Observable systems are useful in this situation. You may better future-proof your systems by designing them with observability in mind as questions emerge and develop over time. A vital component of the problem is an observability pipeline. It allows you to collect the whole universe of data you need and send it cleaned and structured to the many tools your teams require.
It’s time to make a shift
Teams are under increased pressure to achieve results more quickly. On the other hand, faster delivery exposes operations and security teams to deployment risks since they don’t have insight into the modifications developers make across complicated distributed systems. These applications grow less predictable and dependable with time, increasing the risk.
Since incentives across teams aren’t matched, traditional techniques of addressing the information mismatch haven’t succeeded. With LOGIQ, you can gain crucial insight into changing application and infrastructure environments by using pervasive instrumentation and observability methods. Contact us today to know more.