We love data and use this data with our machine based learning algorithms to detect, observe and forecast the anomalies which helps you execute a pro-active (cyber) security strategy.
Our technology integrates seamlessly with existing best of breed systems, tooling, cloud and data base technologies.
On premise, delivered through our SAAS offering or hybrid, you choose, we deliver.
We combined our smartest tools, sensors, analysis and machine learning features into an easy deployable and scalable solution.
We empower you to build a security ecosystem that fits your needs. The tools and underlying features communicate with each other and change their methods based on what happens in and around your systems.
Deploy DutchSec's high-interaction honeypots, build a farm, or deploy any other next-gen sensor, trap and other tricks that come out of the box.
Of course your existing sensors can be simply added.
You are able to manage digital risks and detection mechanisms in a much smarter and simpler way.
But if you are ready to go beyond traditional defense methods, we will empower you to initiate active defense actions -
True deception, hunting for threats and counter-phenomenon defense.
We are ready for it...Are you?
Real-time anomaly detection and alert service that identifies unauthorized or anomalous behavior in network-, application- and log data comes out of the box.
But if used in its full capacity, we support to forecast and mitigate threats before they will affect you...
The most advanced and best of breed analysis solutions out there and the people who use them do their job - but unfortunately only once decisions are made on what data to use or to process and lengthly, costly and time consuming processes on how to access those data and with days, weeks or months on ETL efforts as result.
We understand the need for fast and accurate insights as no other...
We created Raven - a one of a kind smart and highly scalable data streaming engine to get fast result to act on while saving precious time and costs
Rather than ingesting data, we cache them first once we are connected to the data from source or stream.
This will allow our customers to interact with those data without an immediate need to cleanse or to normalize the data first
An initial forensic trail gets created so no track is lost or will get lost along the way - ensuring GDPR compliance as well
Real-time and at any scale as our proprietary engine is designed to do nothing less.
If used as any ETL tool, we empower our users to create, reuse and manage data pipelines and even to embed or tweak existing scripts created by those tools or by in house development efforts - independent from whatever code language is used.
What is truly unique and differentiating is the ability to apply AI/ML and to run preliminary pre-analysis scripts on the data as they are and even before they are ingested to predefined value and relevance
This will save precious time to have preliminary results to act on and will save tremendous amount of time and money on ETL efforts and even costs on data size or processing based licenses on infrastructure or analysis tooling.
The built-in AI/ML components and the human appliance on top will help to learn on what data or data aggregates are relevant and where needed, models will be trained or scripts can be adjusted.
Whatever ETL script you have or whatever script you discover and have ran or tested can be simply applied for scheduled transformation. Existing components can be reused easily
The engine is data base technology agnostic so one-time or scheduled exports can be performed to any data base or warehouse type, any system or any analysis tooling
Our solutions seamlessly connect to each other and with any 3rd party system or solution. We already have made many integrations with Splunk, Elasticsearch, Palantir, (graph) databases, queuing tools, etc.
Instances can be peered between each other: across divisions, industries or companies.
For example Raven enables companies to synchronise high latency Elasticsearch clusters between geo distributed locations without any hassle. Having data being synchronised between Elasticsearch and Splunk is another use case we supported.
Our clients have the option to receive or share intel feeds to whatever level or extend they're comfortable with.
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