Solutions / Log Management

DNIF aggregates all your server logs and metrics into a centralized system in real time. It let's you experience real-time response for searches through massive data volumes and over long time periods.

Analyze any data format

Analyze any data format

Ingest log data from multiple sources without schema limitations, thanks to built-in automatic parsing for common log formats.

Graphs in the blink of an eye

Graphs in the blink of an eye

Interact with millions of data points in plain language and create narratives alongside visualizations at blazing speeds.

ML-powered log analytics

ML-powered log analytics

Uncover patterns and relationships while learning from your data and its interactions.

Robust visualizations

Robust visualizations

Visualize your data using natural language searches. Create intuitive summaries and share them with an easy-to-use interface.

Cost-efficient as you grow

Cost-efficient as you grow

Slab-based pricing bites growing customers with overage charges and penalties. Our subscription-based model offers you scalable collection and predictive pricing.

Track and monitor key metrics

Track and monitor key metrics

Discover the why behind the what. Monitor business performance drivers with interactive dashboards and reports featuring drill-down analysis.

Still struggling with log parsing and scalability issues? What if your SIEM could detect and apply parsers automatically?

Log Management / How It Works

  1. Collect, ingest and parse log data from disparate devices.
  2. Index and store data in a central repository.
  3. Log data is baselined to a common standard and pre-existing rules are executed for primary correlation.
  4. Execute queries to gain deeper insights from data, and define rules to set up reactive triggers.

Log Management / Why You Need It

security and compliance

Security and compliance

Ensure continuous compliance by showing that security controls are working effectively, reducing risk, lowering liability, and proving security control assurance.

proactive monitoring with next gen siem

Proactive monitoring

Monitor key resources and metrics to eliminate small issues before they turn into big problems.

troubleshooting with log data

Troubleshooting with logs

Investigate issues down to their root causes by analyzing them in the context of the entire scope.

data analysis and reporting in log management

Data analysis and reporting

Analyze and visualize your data to answer key questions, track compliance and identify outliers.


Log Management / Why DNIF

detect threats faster

Detect threats faster

Harness the power of automated data enrichment and validation to analyze network, endpoint, asset, user, vulnerability and threat data. Accurately detect both known and unknown threats that other solutions miss.

end-to-end visibility

End-to-end visibility

Ingest data from a variety of log sources without the hassles of schema limitations. Spot trends beyond the limitations of pre-defined rules.

drill down analysis

Drill-down analysis in a few clicks

Allows users to dig through different data layers within a single visualization, and visually analyze data in context.

scalable architecture

Scale without fear

DNIF’s flexible architecture and distributed computing support let's you scale horizontally, with built-in fault tolerance and load balancing.

tactical correlation with next gen siem

Tactical correlation

Take advantage of the multi-threaded search to work through your dataset without having to schedule or save a search to continue at a later time.

anomaly detection

Anomaly detection

Detect outliers based on heuristics and dynamic trend profiles to identify cases that previously went unnoticed.

Are you one of those organizations that's still struggling with implementing effective use cases in your SIEM?

Log Management / Key Features

Experience real-time response for searches through massive data volumes and over long time periods. Analyze the entire range of your log data interactively.

Ingest and parse log data of all types

Logs can be collected, normalized and parsed from a diverse set of devices, servers and applications on the network to get a complete picture across technology stacks.

Search across different logs


Search across data from multiple sources or even data joined from multiple sources.




Contextualize and annotate data points

Clarity of context is rarely provided by a single piece of information in logs. Use additional fields to augment and annotate machine telemetry data.


Scalable architecture


A flexible and adaptive architecture is critical in terms of handling the volume, flow and variety of log data while still delivering operational efficiency.


Real-time monitoring and correlation

Monitoring log data in real time lets teams troubleshoot faster, as teams can now do a time based correlation as per the errors encountered.


Workflow automation


Naturally, manual investigations are slower than automated workflows, which are capable of executing scenario-based checks automatically.



Log Management / FAQs

In many cases, you want to search for metadata: when did they log in? To what server? Is this an anomaly? You want raw logs—but most of the time, making sense out of raw logs is difficult for untrained eyes. Also, if you’re looking for unique events (like a dropped packet, source port, destination port or a combination), context is what matters. Searching based on context and data normalization is critical in this case. This way, you can do this search very quickly against a large data warehouse.

Data retention policies determine how much time must pass before old log events are deleted. If a log is 15 days old and you have retention set to 7 days, that log will be deleted from DNIF. More on data retention policies can be found here .

If you want to be able to search all your logs at once, even when they have different fields, just ship them all to the DNIF data store. Watch out for fields that have the same name but different data types. For example, if one log source has a purely numeric field called "size" while another log source also has a “size” field containing non-numeric values, this will cause issues. DNIF has more than 220+ parsers which are applied automatically based on the type of data ingested.

Yes! DNIF allows you to create custom parsers based on event ID or error number. Basically, anything in a log can be turned into an alert.

No! unlike many of our competitors, DNIF allows you to have full access to all of your log data. Your only limitation is the hardware/VM that you store log data on—but with DNIF’s ability to quickly scale in proportion to your environment, your monitoring needs will always be met.


Security Automation / What Our Customers Say

“DNIF’s Big Data architecture has greatly helped us in gaining visibility at the application level. Integrating SWIFT with DNIF ensured that we were able to meet RBI’s compliance requirements.”

Vinay Tiwari , CISO

RBLBANK-DNIF

“DNIF is re-inventing the SIEM space with its innovative approach of a single unified platform with unlimited scalability that combines advanced analytics, machine learning, threat intelligence and orchestration.”

Sangram Gayal , Partner

PWC-DNIF

“We liked the way DNIF goes beyond traditional tools and validates threats before bringing them to the table. The integration framework is unique and very useful in a large enterprise setup like ours.”

Nitin Chauhan , CTO

RBLBANK-DNIF

“DNIF brings out the real essence of big data to security analytics, this platform can ensure branch offices process their data on prem while having a central hunting / monitoring team respond and resolve threats across the enterprise."

Prashant Maldikar , Head IT Sec

IndusIndBank-DNIF

Log Management / Related Blogs

December 27, 2018

Application Whitelisting - What is it and how it works

In this blog, you’ll learn more about how you can use profilers to automatically detect malicious system processes in DNIF. Click here to know more...

December 10, 2018

How can SIEM Safeguard Your Business from Cyber Threats

Learn how SIEM helps safeguard your business from cybersecurity threats via the use of advanced threat anaytics using machine learning. Know more.

November 27, 2018

What is outlier detection and why you need it

Outlier detection is the process of detecting and subsequently excluding outliers from a given set of data. In this blog, you will discover more about...