Limited and isolated access to endpoint data, event logs and network traffic must be a relic of the past:
Data Fusion is a first-class citizen
Security operations must have flexible access to the full range of enterprise data to keep pace with the increasing number of “unagentable” and other operational enterprise devices, and emerging needs for data contextualization.
Attackers use plenty of evasion methods to make their activities look benign. Without taken a wide range of business data, not known to cyber criminals, as additional context into account, it is almost impossible to reveal masked activities.
Having this in mind…
AI models are at the heart of every AI solution. There is no solution without a model. It is often neglected, however, that model building is only a part of the entire AI lifecycle, and does not have any business impact in itself.
And even worse, data science work is supported by plenty of different frameworks and technologies that are isolated from production.
There is no justification for different technologies in different phases.
Model development requires a continuous “try and learn” and cannot be compared to the software development phase. …
In this article, we introduce an innovative declarative approach to describe AI solutions.
Data processing always made a big step forward with respective flexibility and speed, when software development was augmented by declarative approaches: Declarative business rules and SQL statements are prominent examples of this development.
SAISL, short for Structured AI Solution Language, marks the next big step for modern data processing. It introduces four semantic layers — cases, tasks, workflows and plugins — to enable a holistic declarative description of AI solutions:
From initiating business problems, drivers and desired outcomes to multiple associated business tasks down to AI-driven data…
Suppose you are an AI solution provider, and you constantly implement AI-driven products to help customers optimize current business processes or develop new business opportunities.
Remember the last time a retailer asked you to implement an AI-driven software product to predict the demand of products for the next week in every store. Or a manufacturer to find anomalies in IoT device behavior to take action and mitigate production downtime.
It is the first discussions and initial feasibility phase in which the course is set for a successful project and customer loyalty, or the opposite.
Customers often underestimate the technical complexities…
PredictiveWorks. accelerates the complete AI lifecycle and defines a new standard for unified AI platforms. Data science work alone does not make a business solution, will not augment decisions or create any kind of ROI.
The focus is on the AI triad of data, models and solution, a fast track from experimentation to production and a seamless integration into every specific business environment.
PredictiveWorks. cuts down average project times by over 90% and significantly reduces the size of AI project teams. It is our mission to do AI like cooking with pre-defined ingredients and fully reproducible, version-controlled and reusable solution…
“Every new threat creates a new recipe or modifies an existing one.”
What do you mean? Give me an example. What about bot detection?
No way without embracing AI. And no alternative besides waiting until AI-driven software applications are made available at some point?
This article is not about an exhausting list of AI recipes for IoT threat defense. It is about an innovative approach to build AI solutions on demand and in time without writing a single line of code. To solve a more fundamental problem for threat defense: time-to-value.
Interested? We are excited to share our viewpoints.
AI use cases show great diversity. Not every business problem a priori has an unknown solution and requires research and scientific work. Many companies have similar problems, and these can be solved with reusable AI applications. AI can be like cooking.
In our previous article, we looked into the survey results of renowned analysts with respect to AI in business. Their findings can be viewed as top-level features of a future AI business platform:
Like every new technology trend, AI is perceived as an expert topic. It seems widely accepted, that every business problem needs research and ends in its own AI solution.
In a previous article, we introduced the concept of “AI Cooking” to emphasize that corporate adoption does not mean to embrace research and scientific work at the same time. “AI Cooking” do not deny that AI use cases exist, where research is important to enter unknown regions. It is an approach for smaller companies to respond to common and recurring AI business cases to mitigate their risk to fall behind.
Artificial Intelligence can be like cooking
Europe has 23M SMEs, and according to the EU’s digitization index, more than 1.6M of them plan or already started their digital journey to become data centric.
Terms such as “data centric” ship with so many different meanings that we decided to share our definition to be clear from the very beginning.
We focus on the functional phases how data is used to drive business actions (e.g. decisions or automated commands to control machinery): track, collect, aggregate, analyze, optimize and action. …
PredictiveWorks. is the future of AI adoption: Business driven, organized by a semantic framework and based on Lego-like building blocks. It is an AI assembly line to orchestrate AI applications just like music play lists with a point-and-click user experience.
Built on top of Google’s CDAP and with 200+ data connectors & machine intelligence operators. It supports the new “predict as you train” paradigm and eliminates the expensive and time-consuming gap between training & deployment phase of AI models.
Cyber attacks are one of the most serious digital threats and cost billions of dollars every year. And botnets are a…