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# Introduction

ChatGPT has made Artificial Intelligence popular within a very short time. Anyone can quickly and easily set up an account at OpenAI and get their own impression of the impressive functionalities and capabilities of the Generative Pretrained Transformers (GPT). The precision with which the majority of queries are answered is most impressive.

The question quickly arises as to how companies can benefit from the new AI technologies? After all, the fields of application of generative AI models in enterprises are manifold. These range from applications in the area of customer service and customer dialog, for example, in order to be reachable to its customers 24/7 by being able to answer customer queries automatically, to Speech2Text applications with integrated translation services, to ChatGPT-like query and analysis options for the company's internal data treasure trove. In the status quo, public AI services such as ChatGPT from OpenAI are difficult to imagine in daily enterprise use: on the one hand, this is due to the fact that ChatGPT models lack the domain-specific data of enterprises, and on the other hand, enterprises cannot and do not want to make their internal enterprise data available to a publicly available AI service for various reasons (e.g. data protection, regulation, competition). In this article, we would like to highlight the ways in which Generative AI models can be used to analyze confidential enterprise data and the options available for implementation - on-premise or in private clouds.


## Generative AI for the analysis of sensitive company data

The fields of application of generative AI models are manifold, which is primarily due to the flexibilities of using generative AI. Generative AI models can be used discriminatively in the sense of "Discriminative AI", e.g. by answering questions such as "Is the customer inquiry a complaint or a service request?". But it can also be used to answer generative questions in the sense of classic "Generative AI" such as the dynamic extraction of topics from a customer query "Extract the top three keywords from the text and return only a list of keywords, use the following scheme: keyword 1, keyword 2, keyword 3". By incorporating internal company data and domain-specific data, the precision and quality of generative AI models will be significantly improved - a must-have for reliability in automatically generated answers. Furthermore, when running AI models on-premise or in a private cloud, companies retain complete data sovereignty without being exposed to the risk of both data creators and data owners giving up control and thus the strategic value of their data resources.


## On-Premise Operating Model

One way to use generative AI to analyze sensitive enterprise data is to implement the technology on-premise. In this approach, the AI models and associated infrastructure are operated within the enterprise network. This operating model provides organizations with maximum control over their data and ensures compliance with internal security policies and standards.

The benefits of an on-premise implementation are obvious:

- Strong data security: confidential enterprise data remains within the enterprise boundaries and is subject to the strict security policies of the internal network.
- High flexibility: The infrastructure can be extended or adapted as needed to meet the specific requirements of the enterprise (especially relevant for operators of critical infrastructures or enterprises in the special public interest).
- Lower latency: Since data analysis is performed on-site, latency is minimized, enabling faster processing and analysis.


## Private cloud operating model

As an alternative to on-premise implementations, enterprises can operate Generative AI models for the analysis of sensitive company data in a private cloud. In this operating model, the AI models and required infrastructure are hosted in a dedicated cloud environment used exclusively by the enterprise. Private clouds offer similar benefits to on-premise deployments, but with additional flexibility and cost advantages, such as

- Better scalability: private clouds enable companies to scale resources as needed and adjust the necessary computing capacities according to requirements (e.g. on-demand use of expensive GPU capacities for model training).
- Cost efficiency: By using a private cloud, companies can save costs on purchasing and maintaining their own infrastructure.



## Implementation of Generative AI

For using generative AI in an on-premise or private cloud environment, so-called enterprise AI platforms such as AltaSigma are recommended, which provide all relevant functionalities for the standardized development and efficient operation of generative AI models in one integrated toolbox, e.g.

- Model repositories with generative AI base models (e.g. available from Huggingface),
- Data management functionalities to automate the preparation of relevant enterprise and domain-specific data,
- Automated transfer learning to "inject" the company- and domain-specific knowledge into the AI base models,
- MLOps functionalities for deployment and operation of the Generative AI models,
- Standardized response and request interfaces (REST API), and
- Sophisticated role and rights management.

Enterprise AI platforms come with many functionalities and standards to operate domain-specific Generative AI models in-house, with a similar precision and "look and feel" to that of commercial applications such as ChatGPT.


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Management of Generative AI Models in AltaSigma

## FAQs on Generative AI for the analysis of sensitive enterprise data.

#### How does Generative AI work when analyzing enterprise data?
Generative AI uses so-called deep learning algorithms to train models that can generate new data that resembles the original data. This generated data can then be used for downstream analysis to gain valuable insights.

#### What are the advantages of on-premise implementations compared to private clouds?
On-premise implementations offer organizations greater control over their data and enable tight integration with existing systems. In addition, specific security policies and standards can be better adhered to.

#### What are the benefits of a private cloud implementation?
Private cloud primarily offers enterprises flexibility in scaling resources as well as cost savings through on-demand use of expensive computing capacity (primarily GPU).

#### How can Generative AI contribute to the analysis of sensitive enterprise data?
Aligning Generative AI models with enterprise and domain-specific data enables organizations to significantly increase the precision and quality of Generative AI models. This can significantly improve the quality and reliability of automated decisions as well as the identification of trends and patterns.

#### What security measures should be taken when using Generative AI for sensitive  enterprise data?
It is important to take adequate security measures to control access to sensitive data, apply encryption techniques, and implement strict access and authentication policies. Usually Enterprise AI platforms comply with these security requirements.

#### How to develop custom generative AI models for enterprise data analytics?
Developing customized generative AI models requires a fundamental understanding of the specific requirements of the enterprise and significant knowledge of machine learning. Enterprise AI platforms such as AltaSigma provide all the functionalities to facilitate the development and operation of AI models in a highly resource-efficient manner.


## Conclusion

Leveraging Generative AI to process and analyze sensitive enterprise data enables enterprises to directly participate in current AI developments, with the advantage of gaining valuable insights based on their enterprise data and deriving better automated business decisions. Both on-premise implementations and private clouds offer secure and flexible options for implementing Generative AI, while ensuring data sovereignty. When selecting the appropriate operating model, organizations should consider their specific requirements, security policies, and integration with existing system environments. By properly leveraging Generative AI on their own data, organizations can improve their analytical maturity and significantly increase the quality of data analytics, which will have a positive impact on the current competitive landscape.


How enterprises can retain complete data sovereignty when operating generative AI models.

Generative AI for the analysis of sensitive enterprise data

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# Process Mining - Functionality, benefits, and fields of application

Process mining is a methodology for automatically analyzing and reconstructing process flows from digital event data. AI algorithms are used to X-ray business processes in order to uncover bottlenecks, detect idle times, identify deviations from target processes, and quantify optimization potential.

The areas of application of process mining are manifold, e.g. in manufacturing, logistics, service or health care, in order to analyze business processes and to monitor process conformity.

## Process Mining in a Nutshell

- Digital event data, so-called event logs, are used as the data basis for process mining.
- With process mining, you can perform an automated fact-based analysis of your actual process flows (process discovery).
- Process mining algorithms can reconstruct fact-based process models from event logs, which were previously constructed with comparatively high manual effort.
- The obtained process models can be used for monitoring and quantification of process conformance (e.g. in the context of continuous online monitoring) and for performance analysis.

## What is Process Mining?

In process mining, a fundamental distinction is made between **process analysis (process discovery)**, **process modeling** and the application of process models in the context of **process monitoring (conformance checking)** and **performance analysis**.

- **Process analysis** focuses on the extraction of process information from existing data sources (e.g. from event logs) or other records. The purpose is to survey the actual process flow, identify unnecessary steps or other problems in the process flow in order to increase the efficiency and effectiveness of the processes.
- **Process Modeling** involves training a process model (e.g., in the form of a BPMN model) that reflects the actual steps, dependencies, and resource requirements that occur during the execution of a process. Process models can be automatically reengineered from event logs.
- **Process monitoring (conformance checking)** compares event logs of actual process execution to the process model to identify and locate discrepancies between the process model and actual execution. These discrepancies may indicate that the process is not executing as planned in practice or that the process model is outdated or incorrect.
- **Performance analysis** is used to identify patterns and trends in the process, as well as to identify bottlenecks, delays, or inefficient steps. Based on this analysis, improvements can then be made to the process to continually increase efficiency and effectiveness, thus optimizing process performance.


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AltaSigma Model Management - BPMN Process Model Viewer

## What are the most important algorithms in process mining?

Process mining algorithms are mainly used in the process discovery phase to extract process models from event logs as efficiently as possible. Common algorithms here are **α-algorithms**, **heuristic mining algorithms** and **inductive mining algorithms**. Due to their flexibility, scalability and ability to derive sound process models from event logs, inductive mining algorithms are the most popular methods in the context of process discovery.

## What added value do process mining algorithms provide in practice?

The application of process mining brings a number of benefits if the transaction data underlying the business processes is available and can be converted into an event log format, e.g.

- **Automated modeling of business processes**: time-consuming and tedious modeling of business processes (including with BPMN) is a thing of the past and can be extracted from event logs in the future.
- **Fast explorative analysis and visualization of processes**: Process flows can be easily filtered, visualized and analyzed multidimensionally, as is the case with classic BI applications.
- **Process compliance**: process mining tools can be used to automatically monitor process compliance by identifying process deviations and potential compliance risks at an early stage.
- **Identification of bottlenecks**: by analyzing process data, impending resource bottlenecks can be identified automatically to avoid the occurrence of process inefficiencies.
- **Measurement of process performance**: the analysis of process performance can be automated and continuous to ensure the effectiveness and efficiency of processes.


## Where are typical areas of application for process mining?

Process Mining can be applied in almost every industry where complex processes exist and an efficient and sustainable process optimization helps to secure and strengthen the competitive situation. The following are examples of some application areas for process mining:

- **Financial Services**: Process mining is used in the financial sector to optimize and monitor business processes such as credit applications or compliance checks.
- **Healthcare**: in hospitals, for example, processes such as patient admissions, treatments or billing processes can be analyzed and monitored.
- **Logistics**: typical areas of application can be found in the optimization of order processing or warehouse management.
- **Insurance**: Process mining is used in the insurance industry in the context of claims reporting, billing processes or policy management.
- **Manufacturing**: by using process mining, processes such as material procurement, material flow or quality control can be optimized and an automated value stream analysis can be established.
- **Telecommunications**: applications of process mining can be found in termination management, complaint management, contract management, or incident resolution.
- **Public Sector**: Process mining is used in public authorities to improve processes such as applying for and issuing permits or monitoring rule violations.

The fields of application for process mining are manifold and especially beneficial where complex processes exist and efficient process optimization is required.

## What are the success factors for a successful use of process mining?

When selecting a process mining solution, great care should be taken to ensure that it can be used throughout the company for all relevant business processes. The following aspects in particular play a major and important role:

- Open interfaces as well as support of all common data formats and databases.
- Comprehensive visualization options for process flows, process models, bottlenecks, process metrics and temporal developments.
- Support of all common process discovery algorithms as well as interaction and combination possibilities with all state-of-the-art AI algorithms.
- Automated development of process models (AutoML) and support of standards such as BPMN and Petri Nets.
- Flexible and automated deployment of new and updated process models (MLOps).
- Process Model Governance and Process Model Lifecycle Management.
- Open interfaces (e.g. REST API) for online monitoring of live processes and easy integration of results into third party systems.

AltaSigma provides all these core process mining functionalities for analyzing, monitoring and optimizing processes from an open platform to make process management easier, faster, better and more efficient in the future.

How to use process mining to automatically visualize process flows, analyze value streams, and reconstruct process models from digital event data.

AltaSigma Blog

Generative AI for the analysis of sensitive enterprise data

Process Mining - Functionality, benefits, and fields of application