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ManageEngine Advances Security Intelligence with Log Data for Third-Party Tools

ManageEngine announced a new API that enables third-party tools to access log data generated by EventLog Analyzer, its security information and event management (SIEM) solution.

Available immediately, the EventLog Analyzer API lets security administrators feed reams of normalized log data into any third-party application, including crowd-sourced threat intelligence solutions, vulnerability assessment platforms, business intelligence tools or even custom applications for advanced security intelligence and threat protection.

Though SIEM solutions have been offering provisions to import data from varied sources, such integrations are fraught with many limitations. In the absence of proper correlation and data processing, feeding terabytes of data to the SIEM solution will not offer the required protection. EventLog Analyzer shatters all these limitations by opening up its database for integration with any third-party application.

Security administrators can leverage this integration to bolster their security framework in such use cases as:

- Advanced threat mitigation – The normalized data from EventLog Analyzer could be fed into crowd-sourced advanced threat intelligence services, sandbox solutions or sophisticated vulnerability assessment platforms. These tools can associate EventLog Analyzer’s security data with the information they already possess and help mitigate emerging attacks, botnets, zero-day threats, phishing attacks, malware attacks and advanced persistent threats (APT).

- Location-based threat analysis – Integration with geolocation services could help enterprises gain geographic context to any event. This, in turn, helps pinpoint the country of origin and physical location of an application involved in an event. If the origin matches the countries commonly associated with APTs, suspicious traffic could be isolated for deeper analysis.

- Customized security views – Security managers could even create their own web applications and dashboards by extracting the data critical to their needs.

- Application performance tuning – Normalized data from EventLog Analyzer could be fed into modern business intelligence tools, which could help organizations understand the evolving threat landscape, assess risks and prepare mitigation strategy and an emergency response plan in the event of attack. The data could also help drill down to overall application performance issues and assess product usability and quality.

EventLog Analyzer provides Thrift IDL-based APIs which security administrators can use to pull all required data and achieve integration. The power of the API has been demonstrated through a Python-based client as the reference implementation.

EventLog Analyzer collects, normalizes, analyzes, correlates and stores voluminous logs from heterogeneous sources. Now, the API can provide actionable intelligence and help security admins trace, thwart and combat evolving threats.

The API is available immediately and works with EventLog Analyzer v 9.0. Users can submit a request to access the API, and the EventLog Analyzer technical support team will get in touch with them.

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ManageEngine Advances Security Intelligence with Log Data for Third-Party Tools

ManageEngine announced a new API that enables third-party tools to access log data generated by EventLog Analyzer, its security information and event management (SIEM) solution.

Available immediately, the EventLog Analyzer API lets security administrators feed reams of normalized log data into any third-party application, including crowd-sourced threat intelligence solutions, vulnerability assessment platforms, business intelligence tools or even custom applications for advanced security intelligence and threat protection.

Though SIEM solutions have been offering provisions to import data from varied sources, such integrations are fraught with many limitations. In the absence of proper correlation and data processing, feeding terabytes of data to the SIEM solution will not offer the required protection. EventLog Analyzer shatters all these limitations by opening up its database for integration with any third-party application.

Security administrators can leverage this integration to bolster their security framework in such use cases as:

- Advanced threat mitigation – The normalized data from EventLog Analyzer could be fed into crowd-sourced advanced threat intelligence services, sandbox solutions or sophisticated vulnerability assessment platforms. These tools can associate EventLog Analyzer’s security data with the information they already possess and help mitigate emerging attacks, botnets, zero-day threats, phishing attacks, malware attacks and advanced persistent threats (APT).

- Location-based threat analysis – Integration with geolocation services could help enterprises gain geographic context to any event. This, in turn, helps pinpoint the country of origin and physical location of an application involved in an event. If the origin matches the countries commonly associated with APTs, suspicious traffic could be isolated for deeper analysis.

- Customized security views – Security managers could even create their own web applications and dashboards by extracting the data critical to their needs.

- Application performance tuning – Normalized data from EventLog Analyzer could be fed into modern business intelligence tools, which could help organizations understand the evolving threat landscape, assess risks and prepare mitigation strategy and an emergency response plan in the event of attack. The data could also help drill down to overall application performance issues and assess product usability and quality.

EventLog Analyzer provides Thrift IDL-based APIs which security administrators can use to pull all required data and achieve integration. The power of the API has been demonstrated through a Python-based client as the reference implementation.

EventLog Analyzer collects, normalizes, analyzes, correlates and stores voluminous logs from heterogeneous sources. Now, the API can provide actionable intelligence and help security admins trace, thwart and combat evolving threats.

The API is available immediately and works with EventLog Analyzer v 9.0. Users can submit a request to access the API, and the EventLog Analyzer technical support team will get in touch with them.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...