Use Case: Manufacturing Industry 2023

Protecting intellectual property across global networks and data formats.

Use Case: Manufacturing Industry 2023


  • Multiple Breaches
  • Duplicates of IP Data
  • Data sprawl across the globe
  • No view of unstructured data
  • Full unstructured data scan discover, classify and catalogue data
  • Full data inventory using AI ML
  • Full data risk assessment
  • Enhanced context for DLP and DRM

Data Challenges for Manufacturing organisations

Global manufacturing organisations face a variety of data challenges that impact their operations, decision-making processes, and overall efficiency. These challenges encompass a wide range of issues, from data integration to regulatory compliance.

Data integration poses a significant hurdle, as manufacturing operations generate data from various sources, such as sensors, machines, and supply chain systems, which must be harmonised for effective analysis. Furthermore, the sheer volume of data generated by manufacturing processes can be overwhelming, requiring substantial resources for storage and management.

Ensuring data quality is crucial, as inaccurate or inconsistent data can lead to errors and inefficiencies in production and decision-making. Real-time data availability is essential for process control, quality assurance, and predictive maintenance, but it can be challenging to achieve.

Data security is another major concern, given the sensitivity of manufacturing data, including intellectual property and customer information. Protecting this data from cyber threats and unauthorised access is paramount.

Manufacturing organisations often grapple with legacy systems that may not easily integrate with modern data technologies. Additionally, deriving valuable insights from manufacturing data requires the right tools and expertise, making analytics and interpretation a critical challenge.

Scalability is crucial as organisations grow, and supply chain complexity, compliance with regulations, and the existence of data silos further compound the challenges. Embracing emerging technologies like IoT and Industry 4.0 introduces new complexities.

To address these issues, manufacturing organisations invest in data management strategies, data analytics tools, cybersecurity measures, and data governance practices. They also explore AI and machine learning to extract actionable insights from their data, ultimately aiming to improve efficiency and competitiveness in the rapidly evolving global manufacturing landscape.

The Need for Assessing and Protecting IP

Manufacturing organisations today operate globally either in size or through its network of suppliers and clients. The increase with shareable cloud based information, which is crucial to the development of innovation, collaboration and supply, is the target of many ransomware attacks for IP.

In the context of a global manufacturing organisation, assessing and protecting intellectual property (IP) is crucial for preserving innovation and maintaining its competitive edge by safeguarding its proprietary technologies, processes, and products. IP protection can attract investment, as investors prefer organisations that have secured their IP, leading to increased access to capital for research and expansion, which can be monetized through licensing, generating revenue. In some cases, IP protection may have national security implications, necessitating stringent security measures to prevent unauthorised access or misuse of sensitive manufacturing technologies that could be used in global infrastructure, medical, or other significant global industries. In a global manufacturing context, IP protection is fundamental for innovation, competitiveness, and sustainable growth and needs to be protected at the source where it is created, managed and stored.

The barriers to protecting IP effectively include allocating and controlling storage cost both on-prem and in the cloud, data discovery and management, Lack of cloud security skills/expertise, Integrating cloud resources with on-premises systems, and meeting both governance and compliance requirements. Both at scale and across language and region barriers.


Getvisibility's customisable AI revolutionises the data landscape with cutting-edge machine learning and user-friendly interfaces, to empower businesses to unlock unprecedented insights, across Data Security Posture Management (DSPM), Data AccessGovernance (DAG), Data Detection and Response (DDR), Privacy And Compliance.

Getvisibility’s platform allows organisations to meet compliance deadlines by defining and implementing data policies, at speed, with EUC, and making strategic data decisions via automated continuous compliance reporting.

This is first achieved via Getvisibility Data Guard, a Data Risk Assessment and Protection solution that, unlike other DLP solutions, defines and creates Data Policy with Data Risk Analysis via Dashboards. This can save customers 100 weeks of work in defining and implementing a compliant data policy for general and IP data that requires specific and secure data management. Getvilibilty Data Guard is a three step online platform that can provide risk analysis to all stakeholders with an automated data policy within a week.

Getvisibility Focus uses machine learning (ML) to specifically discover and classify data across an organisation and its network via tailored AI and out of the box connectors that connect to customers data storage platform. These connectors are also tailored for any IP or data storage platforms, across multiple languages. Data created in different languages can be discovered and classified using Getvisibiliity’s ML language library.

The ongoing automation of policy compliance, once defined and set-up by Data Guard, is performed by Getvisibility Synergy. This allows real time classification of data as it is created. Again using out of the box connectors to enable EUC enabling an organisation to classify and protect data in use, new data, and data in motion.

When Data Guard is combined with Synergy it allows EUC to be monitored in real-time as documents and data are created and edited. This enables organisations to fully improve data efficiency, comply with regulations, and maintain stability of business operations through risk management of IP.

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