5GAI/MLBig DataDigitial Transformation

Transforming Telecom with AIOps: Use Cases and Applications!

AIOps stands for Artificial Intelligence for IT Operations. It is a technology that combines machine learning, big data analytics, and other artificial intelligence techniques to automate and improve IT operations. AIOps is designed to help organizations manage and optimize their IT infrastructure, applications, and services more efficiently and effectively.

AIOps uses advanced analytics and machine learning algorithms to analyze large volumes of data generated by IT systems and applications. It can identify patterns, detect anomalies, and provide insights into the root cause of issues. AIOps can also automate routine tasks, such as monitoring, alerting, and incident management, freeing up IT staff to focus on more strategic activities.

One of the key challenges faced by modern organization is processing infinite amount of data, which could easily overwhelm operations teams. Moreover, beyond just processing, making sense of information, esp. Operational awareness is critical for success of today’s IT Operations.

Another key driver for adoption of AIOps in modern IT Organization, is adoption of multi-cloud. While many organizations, including Telco Service providers, still have large on-prem deployments, public cloud/hybrid cloud has entered the premises, where few workloads have already made shift to Cloud. Operationalizing (Day1/2 and beyond) multi-cloud environments, with enormous amount of data gathered, is next level challenges IT Ops teams must tackle daily.

While AIOps solutions catering to modern IT Organization, for Telco Service providers, AIOps is domain specific challenge to solve. The main difference is data they directly collect and use cases they solve, beyond typical AIOps Use cases offered in IT Organizations. It could be termed as Domain specific AIOps, while Domain Agnostic AIOps caters to wider IT landscape and use cases.

Components of AIOps: Gigaom AIOps Trends Report (2023)

In the telecom industry, AIOps can be used to improve network performance, reduce downtime, and enhance the customer experience. AIOps can help telecom operators monitor and manage their network infrastructure, detect, and resolve issues in real-time, and optimize network resources to meet changing demands. AIOps can also help telecom operators analyze customer data to gain insights into customer behavior and preferences, enabling them to offer more personalized services and improve customer satisfaction.

AIOps is a rapidly evolving field, and new use cases and applications are emerging all the time. Some of the key trends in AIOps include the use of AI and ML to automate IT operations processes, the integration of AIOps with DevOps and other IT processes, and the use of AIOps to improve security and compliance. As AIOps continues to evolve, it is likely to become an increasingly valuable tool for IT teams in a wide range of industries, including telecom.

How AIOps differs with Modern IT Operations in Telecom:

One big question arises among many Telco Service providers is that, how AIOps differs from their tradition Operations. It’s quite an interesting question. Let’s discuss it briefly.

If you look at modern IT or even Telecom Service providers, Monitoring or even modern Observability stack is quite siloed. Multiple vendors/partners have built solutions to monitor and operationalize their own stacks. E.g. Network Vendors such as Cisco or Juniper offer Network Observability data of their devices separately or even Platform/Cloud Providers, offer Platform level metrics and logs stored in their respective Observability solutions. In addition, Application providers, they offer their application specific monitoring tools, which are also siloed in most cases.

For Service providers, it creates a huge challenge to get complete picture of their entire network and organization at one place. Moreover, amount of data needs to collect, gathered from multitude of systems, and processed at one place such as central data lake is daunting task for them.

Modern AIOps tends to solve these sets of challenge. Irrespective of if you have implemented central Observability solutions (refer ELK or EFK stack) in your organization, AIOps solutions can collect, gather, and process infinite amount of data with help of advance AI/ML/Deep Learning Algorithms and help to generate actionable insights from the data quickly. It certainly helps to leverage central data warehouses or data lakes, if already available, but we have seen many AIOps solutions doesn’t mandate to have your own data lake built, which is quite a big relief for most of the service provider.

Moreover, as mentioned, many service providers have adopted multi hybrid cloud solutions for their 4G/5G deployments, which create Operational silos and overheads. AIOps solutions can address these challenges with multi-cloud, multi-platform metrics collection and offer single pane of glass to Operational team without worrying about siloed monitoring stacks offered by multiple partners.

AIOps Use Cases in Telecom

AIOps in Telecom has several use cases that can help telecom companies to improve their operations and customer experience. One of the primary use cases is network management. AIOps can help telecom companies to monitor their network infrastructure, identify issues, and resolve them proactively. This can help to reduce downtime, improve network performance, and enhance customer satisfaction.

Another use case of AIOps in Telecom is customer service. AIOps can help telecom companies to analyze customer data and provide personalized recommendations to customers. This can help to improve customer satisfaction and reduce churn rate. AIOps can also help to automate customer service processes, such as ticket routing and resolution, which can save time and resources for telecom companies.

One of the most popular use cases of AIOps in Telecom is predictive maintenance & fault remediation. AIOps can help telecom companies to predict equipment failures and schedule maintenance activities proactively. This can help to reduce downtime, improve network performance, and save costs for telecom companies. AIOps can also help to optimize maintenance schedules based on real-time data, which can further improve efficiency and reduce costs.

There are few upcoming trends in Telecom including sustainability, driving AIOps in Telecom to gather power consumption data across various topologies, analyse it and predict the future power consumption or drive power efficiency to automate life cycle of various infra components. Red Hat Inc, the Open-Source Software giant, has recently announced one such CNCF initiative, Project Kepler, which gathers various energy level metrices of Kubernetes clusters including Pods and Nodes, and export it. Red Hat intends to integrate Kepler in future OpenShift releases.

Finally, AIOps can be used for capacity planning in the Telecom industry. AIOps can help telecom companies to analyze network traffic data and predict future demand for network resources. This can help to optimize network capacity and ensure that telecom companies can meet the growing demand for data services. AIOps can also help to identify underutilized resources and optimize resource allocation, which can further reduce costs for telecom companies.

Benefits of using AIOps

AIOps in Telecom offers several benefits over traditional methods. One of the primary advantages is the ability to automate and streamline operations. AIOps can monitor and analyze vast amounts of data in real-time, allowing for faster and more accurate decision-making. This can lead to increased efficiency, reduced downtime, and improved customer satisfaction.

The benefit to automate and streamline remediation (Respond) is becoming critical aspect in evaluating benefits of AIOps solutions. Many IT Organization and Telco Service providers are adopting various IT Automation solutions including Ansible Automation to automate various manual repetitive tasks including various Operational processes. One such example is Red Hat Event Driven Ansible, announced in Red Hat Summit 2023. With event driven Ansible, now, AIOps solutions such as Dynatrace AIOps, can trigger execution of certain Ansible Playbooks directly without any manual intervention, leading to auto-remediation of operational issues discovered by AIOps solutions.

AIOps in Telecom can also help to reduce costs by optimizing resource utilization. By analyzing data on network traffic, usage patterns, and other factors, AIOps can identify areas where resources are being underutilized or overprovisioned. This information can be used to make more informed decisions about resource allocation, leading to cost savings and improved efficiency.

Another benefit of AIOps in Telecom is the ability to detect and resolve issues before they become critical. According to IBM, AIOps can reduce network downtime by up to 80%. A By leveraging machine learning and predictive analytics, AIOps can identify patterns and anomalies in data that may indicate a potential problem. This allows for proactive measures to be taken to prevent issues from occurring, rather than simply reacting to them after the fact.

Finally, AIOps in Telecom can help to improve the overall quality of service. By monitoring and analyzing data on network performance, customer behavior, and other factors, AIOps can identify areas where improvements can be made. This can lead to better service delivery, increased customer satisfaction, and improved business outcomes.

How Telcos using AIOps

Telecom companies are currently implementing AIOps in their operations in numerous ways. One of the most common use cases is in network management. AIOps can help telecom companies to monitor their networks in real-time, identify potential issues, and take proactive measures to prevent downtime. This can lead to improved network performance, increased customer satisfaction, and reduced costs. And as mentioned above, Predictive Maintenance and Fault remediation is one the popular use cases under network management category.

Telecom companies are also using AIOps in their marketing and sales operations. AIOps can help to analyze customer data and provide insights into customer behavior and preferences. This can help telecom companies to develop targeted marketing campaigns and improve their sales strategies.

Another way that telecom companies are using AIOps is in customer service. AIOps can help to automate customer service processes, such as chatbots and virtual assistants, which can provide customers with quick and efficient support. This can lead to improved customer satisfaction and reduced costs for telecom companies.

Finally, telecom companies are using AIOps in their security operations. AIOps can help to detect and respond to security threats in real-time, which can help to prevent data breaches and other security incidents. This can lead to improved security and reduced costs for telecom companies.

Trends in AIOps for Telcos

A trend that is emerging in the use of AIOps in the Telecom industry is the integration of machine learning and artificial intelligence algorithms into network operations. This allows for real-time monitoring and analysis of network performance, enabling proactive identification and resolution of issues before they impact customers.

According to BMC, approx. 75% of telecom organization plan to adopt AIOps in near future. Moreover, there are certain market reports, which states that telecom industry is expected to be one of the fastest-growing sectors in AIOps adoption.

One major trend we see clearly emerging is the use of AIOps for predictive maintenance and fault rectification/remediation. By analyzing data from network devices and sensors, equipment providers, ticketing systems, weather monitoring system etc AIOps tools can identify potential equipment failures before they occur, allowing for proactive maintenance and reducing downtime. Moreover, there’s growing trend to use IT Automation tools such as Ansible Automation to automate auto-remediation of Operational issues, discovered by AIOps solutions, without manual intervention.

Telecom companies are also using AIOps to improve customer experience. By analyzing customer data and behavior, AIOps can provide personalized recommendations and solutions, as well as identify potential issues before they become complaints. AIOps is also being used for network automation, enabling the creation of self-healing networks that can automatically detect and resolve issues without human intervention. This reduces the need for manual troubleshooting and allows for faster resolution times.

Moreover, we do see growing interest of using AIOps solutions to drive Telco sustainability goals. It’s likely to drive AIOps adoption faster among Telco service providers, esp. where tools such as Red Hat Kepler capturing Kubernetes Pod and Node power metrics and export it for further analysis and automate infra life cycles.

Finally, there is a trend towards the use of AIOps for security and fraud detection. By analyzing network traffic and user behavior, AIOps can identify potential security threats and fraudulent activity, enabling proactive measures to be taken to prevent them.

(All views are personal. All reference citations are for reference purpose and owned by respective Organization).

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AI/MLNext Gen Telecom

The Future of Telecom: Leveraging Generative AI for Success

Generative AI is a subset of artificial intelligence that involves the use of algorithms to generate new content, such as images, videos, and text. Unlike traditional AI, which relies on pre-existing data to make decisions, generative AI can create new data from scratch. This is achieved using deep learning algorithms, which are designed to mimic the way the human brain works. By analyzing enormous amounts of data, these algorithms can learn to recognize patterns and generate new content based on those patterns.

Generative AI works by using a neural network to analyze and learn from large datasets. The neural network is made up of layers of interconnected nodes, each of which performs a specific function. The first layer of the network takes in raw data, such as an image or a piece of text, and processes it in a way that makes it easier for the subsequent layers to analyze. Each subsequent layer builds on the work of the previous layer, gradually refining the data until the network can generate new content that is like the original data.

There are many diverse types of generative AI algorithms, each of which is designed to generate a specific type of content. For example, some algorithms are designed to generate realistic images, while others are designed to generate natural language text. Regardless of the type of algorithm, however, the goal is always the same: to create new content that is indistinguishable from content created by humans.

What are potential use cases of Generative AI for Telcos?

Generative AI has many potential use cases for telcos. For example, it could be used to generate personalized marketing content for individual customers, or to create virtual assistants that can interact with customers in a more natural way. It could also be used to generate new products and services based on customer data, or to analyze large datasets to identify trends and patterns that could be used to improve network performance. Overall, generative AI has the potential to revolutionize the way that telcos operate, by enabling them to create new content and services that are tailored to the needs of individual customers.

Generative AI has the potential to revolutionize the telecommunications industry by enabling telcos to automate and optimize various processes. One of the key benefits of using generative AI is that it can help telcos to improve their network performance by predicting and preventing network outages. This is achieved by analyzing large amounts of data from network devices and identifying patterns that could lead to outages. By proactively addressing these issues, telcos can reduce downtime and improve customer satisfaction.

Another potential benefit of using generative AI in the telecommunications industry is that it can help telcos to personalize their services and offerings. By analyzing customer data, generative AI can identify patterns and preferences that can be used to create personalized recommendations and offers. This can help telcos to improve customer loyalty and retention, as well as increase revenue by offering targeted promotions and services.

Generative AI can also help telcos to optimize their operations by automating various processes. For example, it can be used to automate customer service interactions, such as chatbots that can handle simple queries and requests. This can help to reduce the workload on human customer service agents, freeing them up to handle more complex issues. Additionally, generative AI can be used to optimize network routing and traffic management, which can help to improve network performance and reduce costs.

Finally, generative AI can help telcos to improve their security and prevent fraud. By analyzing network traffic and customer data, generative AI can identify potential security threats and fraudulent activity. This can help telcos to take proactive measures to prevent these issues, such as blocking suspicious traffic or alerting customers to potential fraud attempts. By improving security and preventing fraud, telcos can protect their customers and their reputation, as well as reduce financial losses.

Real world examples of Generative AI in Telecom:

There are many examples of real-world examples of generative AI used in Telecom as on today.

Generative AI has been successfully used in the telecom industry to improve customer experience. One example is the use of chatbots powered by generative AI to provide 24/7 customer support. These chatbots can understand natural language and provide personalized responses to customers, reducing the need for human intervention and improving response times.

Another successful use case of generative AI in telecom is predictive maintenance. By analyzing data from network equipment, generative AI algorithms can predict when equipment is likely to fail and alert technicians to perform maintenance before a failure occurs. This reduces downtime and improves network reliability.

Generative AI has also been used to optimize network performance. By analyzing network data in real-time, generative AI algorithms can identify network congestion and adjust network resources to improve performance. This can result in faster data transfer speeds and improved overall network performance.

Finally, generative AI has been used in the telecom industry to improve fraud detection. By analyzing call data records and other network data, generative AI algorithms can identify patterns of fraudulent activity and alert network operators to act. This can help reduce losses due to fraud and improve overall network security.

Challenges & Opportunities of implementing Generative AI in Telecom:

Implementing generative AI in the telecom industry presents several challenges. One of the main challenges is the lack of quality data. Generative AI requires enormous amounts of high-quality data to train the models effectively. However, the telecom industry generates vast amounts of data, but not all of it is relevant or useful for generative AI. Therefore, telecom companies need to invest in data quality management to ensure that the data used for generative AI is accurate, complete, and relevant.

Another challenge is the complexity of the telecom industry. The telecom industry is overly complex, with multiple layers of technology, including hardware, software, and networks. This complexity makes it difficult to implement generative AI, which requires a deep understanding of the underlying technology. Telecom companies need to invest in specialized talent and expertise to develop and implement generative AI solutions effectively.

Data privacy and security is another challenge when implementing generative AI in the telecom industry. Telecom companies collect and store vast amounts of sensitive customer data, including personal information, call records, and location data. This data must be protected from cyber threats and breaches. Therefore, telecom companies need to ensure that their generative AI solutions comply with data privacy and security regulations and implement robust security measures to protect customer data.

Finally, the cost of implementing generative AI in the telecom industry is a significant challenge. Developing and implementing generative AI solutions requires significant investment in technology, talent, and infrastructure. Telecom companies need to carefully evaluate the potential benefits of generative AI against the costs and risks of implementation. They need to ensure that the benefits of generative AI outweigh the costs and that the investment aligns with their business objectives and strategy.

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MEC Use Case Review- How AWS delivered Smart Factory with Wavelength Zone!

(The blog post is series of blogs, which talks about various Public Cloud Providers Edge/MEC Offerings from use case perspective. In this blog, we will explore AWS Wavelength based Smart Factory Solution.)

As edge computing race is heating up, AWS, the biggest cloud provider on the planet, has rolled out couple of solutions to meet the requirements of edge use cases. According to AWS, it categorized its edge offerings into, three distinct infrastructure setups (from Telco perspective), namely (there are many other offerings, but for sake of this post, we will only focus on specific offerings):

  1. AWS Wavelength (or Wavelength Zone): These are AWS Infrastructure Zones, built for a specific Telco and infrastructure is part of Telco’s 4G/5G Network. Currently AWS offers these zones for Verizon in US, KDDI in Japan, SK Telecom in South Korea, and Vodafone in UK. From Telco perspective, it will be a Public MEC deployment at Telco Network Edge.
  2. AWS Outposts: This offering is more of deploying entire AWS Infra stack right on-premises of the specific customer, a private network deployment. From Telco perspective, it will be a Private MEC deployment.
  3. AWS Local Zones: This is an interesting offering from AWS, something quite like Wavelength Zones but built outside telco’s network (or not specific to Telco), within AWS Infra region. Any telco/MVNO or Enterprise can leverage these low latency local zones for different edge use cases. From Telco perspective, it will be a Public MEC deployment, outside Telco Network Edge, as traffic from device app could travel outside Telco network before it reaches backend edge/MEC app.

AWS talks at length on its product pages about choosing right offering for specific use cases, locations, and other details at great length which we won’t discuss here. The purpose of this post to discuss a specific use case at length deployed with AWS Wavelength.

Currently, from US perspective, Verizon is using Wavelength Zones for MEC/Edge Use cases while another greenfield operator Dish Wireless announced that they will be using AWS Outposts and Local Zones for Edge/MEC deployments.

Choosing the right offering from a telco perspective could be a great dilemma, although AWS does talk about it briefly in FAQ section, of all offerings, highlighting the differences, but it doesn’t make distinction very clearly in terms of end-to-end network latency with all three. Although from re-presentation perspective, shown below, it’s safer to assume that AWS Outposts, being on-premises, offers the best network latencies while Local Zones offer much higher latencies among three. The actual latencies could vary and needs further investigation.

AWS Outposts vs Wavelength vs Local Zone (Representation purpose): Source: AWS

Smart Factory with AWS Wavelength

For a Smart Factory Use Case, the factory setup shown below, and has distinct set of sensors, cameras, and vision systems, supplied by different vendors. Among numerous challenges currently faced by the factory, some of them include, multi-vendor system integration, high latency (cloud-based system for processing sensor data), security, and scalability (plant expansion, cabling etc. issue).

Smart Factory Multi-Campus setup (Source: AWS)

AWS has chosen Wavelength Zone (mostly with a Telco) to deliver low latency and higher throughput (Video streams) to deliver remote inspection use case for the factory.

In the setup, cameras from factory plants stream live video streams to AWS Wavelength Zone infrastructure over 5G. At the zone, there’s AWS Infra, running Video Analytics Inferencing pipeline, detects defects in production line, notifies the plant operator, over 5G, in real-time. Video streams are also sent to cloud storage (S3).

AWS Wavelength Smart Factory Setup and Workflow (Source: AWS)

AWS highlights that, choosing Wavelength Zone is right approach here, compared to Outposts based solution (on-premises Edge), for economic reasons. Moreover, in this setup, factory doesn’t have to operate or maintain the infra, which resides at a Wavelength Zone, or telco operator’s network edge.

The setup above is an example of Public MEC deployment by a telco, for a factory, with the help of cloud service provider (AWS), also involving an ISV (TCS) who built the MEC App or Video Analytics solution. The partnership model, demonstrated by AWS, is in-line with our thought process outlined in my previous post. Although it’s not clear what’s network latency achieved compared to cloud setup, but we assume it meets criteria of delivering use case at network edge. Moreover, as economic reasons weigh more important here, had AWS chosen Outposts (Private MEC) based deployments, we believe the latency and throughput could have been better than Public MEC setup.

On the 5G side, as this is Public MEC setup, the video streams from factory campus (RTSP) streamed to Wavelength Zone over 5G. There’s no clarity if Wavelength Zone is co-located with 5G gNB but it’s safe to assume, there’s no delay in transfer of video streams from gNB to Wavelength Zone. Another aspect which isn’t clearly mentioned about 5GC, and we assume, 5GC resides within telco’s central DC and not deployed at Wavelength Zone itself (unlike Private MEC setup). This needs to be verified as it could add additional delay, esp. from control path setup but less on data path. Moreover, it’s also not clear, if AWS Wavelength Zone to AWS Cloud connectivity is using 5G or AWS Direct Connect to transfer video streams to S3 buckets.


AWS offers different solutions for deployment of Edge/MEC Applications, namely Outposts, Wavelength Zone and Local Zone for Telcos. Wavelength Zone based solution offers many advantages, esp. it brings AWS Compute, Storage, and network resources closer to customer (Devices), compared to cloud setup, improving end-to-end latencies, important from edge/MEC perspective, improving overall user experience. Moreover, Wavelength Zone is part of Telco Network setup, so app traffic doesn’t travel outside telco network making it more secure architecture. It also doesn’t require end customer to deploy its own cloud infra/edge setup on-premises, and leverages telco setup, offering better turnaround time. In the end, Wavelength Zone is part of AWS Region architecture, offering required resiliency for edge data center setup, which often runs mission critical services.

On the downside, we clearly see, this particular use case could be better off on AWS Outposts, which would offer lesser latencies compared to Wavelength Zone. It would require factory to setup its own Private 5G Network or Hybrid setup could also be possible. Moreover, outposts also offer better security and data governance as data entirely resides within on-premises, reducing network bandwidth usages as well.

Overall actual latency varies from each type of deployment, outposts offering the ultra-low, while local zones offer medium latencies. Depending on the use case and required latency, telcos need to select appropriate deployment option, as no option is better than other, in consultation with end customer’s requirement. There are other deciding factors, including time to market, CAPEX, data privacy & security, and complexity of setup, to look at before choosing the solution.

In summary, AWS has demonstrated the successful 5G-MEC partnership model to deliver, Smart Factory Use Case, with Wavelength Zone Public MEC Setup over 5G.

AWS Edge Offerings Comparison (Source: Telecomblogs Media)

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5GAI/MLCyber Security

5G and AI Expected to Bring Heightened Cybersecurity Risks, Study Finds

An overwhelming majority of cybersecurity and risk management leaders believe that developments in 5G wireless technology will create cybersecurity challenges for their organizations. Their top three 5G-related concerns are greater risk of attacks on Internet of Things (IoT) networks, a wider attack surface and a lack of security by design in 5G hardware and firmware.

These are among the findings of a new report released today by Information Risk Management (IRM), a UK-based cybersecurity company of Altran, the global leader in engineering and R&D services.

The report, titled Risky Business,is based on a survey of senior cybersecurity and risk management decision makers at 50 global companies across seven major industry sectors: automotive, communications, energy, finance/public sector, software/internet, transport and pharmaceuticals. The study was conducted between July and September of this year.

Eighty-three percent of survey respondents said 5G developments will create cybersecurity challenges for their organizations, suggesting that the new technology will bring heightened risks. “The acceleration to market of 5G and lack of security considerations are causing concern,” the report states. “The vulnerabilities in 5G appear to go beyond wireless, introducing risks around virtualised and cloud native infrastructure.”

The study also found that 86% of respondents expect artificial intelligence (AI) to have an impact on their cybersecurity strategy over the next five years, as AI systems are integrated into core enterprise security functions. The top three AI applications that respondents said they would consider implementing as part of their cybersecurity strategy are network intrusion detection and prevention, fraud detection and secure user authentication.

AI in cybersecurity is a double-edged sword,” the report explains. “It can provide many companies with the tools to detect fraudulent activity on bank accounts, for example, but it is inevitably a tool being used by cybercriminals to carry out even more sophisticated attacks.”

In late August, for example, The Wall Street Journal reported that criminals using AI-based software had successfully mimicked a German CEO’s voice and had duped the head of a UK subsidiary into sending €220,000 ($243,000) to a fraudulent account. It is being dubbed one of the world’s first publicly known cyberattacks using AI. “We are likely to see more of this as the technology develops,” the report warns.

Commenting on the potential impact of 5G and AI on cybersecurity, Charles White, CEO of IRM, cautioned: “A lack of awareness of these technologies’ security implications can have far reaching consequences. At best an embarrassing fine and at worst a fatal blow to the bottom line. Now is the time for enterprises to work closely with their cybersecurity teams to design and develop 5G and AI products that place cybersecurity front and center.”

The study also found:

  • A growing number of C-level executives recognize the challenges facing enterprise security teams. Ninety-one percent of respondents said that increased cybersecurity awareness at the C-level has translated into their decision-making. But most cybersecurity decisions are still based on cost – and not on the safest solutions to put in place, according to respondents, indicating a lack of understanding of the financial and reputational impact of cyberattacks.
  • There is a worrisome lack of awareness of the Networks & Information Systems Directive/ Network & Information Systems Regulations, which is a piece of legislation setting a range of network and information security requirements for Operators of Essential Services (OES) and Digital Service Providers (DSPs). The survey found that 30% of respondents are unaware of the NIS Directive/Regulations, and of the 70% who are aware of the legislation, over a third (about 25% overall) have failed to implement the necessary changes.

IRM is at the heart of Altran’s recently formed World Class Center for Cybersecurity, which offers an extended portfolio of global solutions to protect next-generation networks and systems. With sites in North America, France, the UK and Portugal, the WCC for cybersecurity specializes in working with some of the world’s largest organizations to combat cyber challenges introduced by Industry 4.0.

To download a copy of the report, please visit

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