I recently had a debate with a colleague on the critical materials that will be needed to support our #sustainability transformation.

One of these materials is certainly #graphite, a derivative of carbon renowned for its excellent electrical conductivity, high-temperature stability and chemical inertness.
Although we often mostly mention Lithium, Graphite stands out as a game changer in the energy storage technology, particularly the production of lithium-ion batteries which are central to the electric car industry. Few people know that each current electric battery is made of 60 to 90 kilogrammes of Graphite.

And yet close to 90% of the world’s graphite is currently being refined in China, which recently decided to tighten its export restrictions of this highly strategic material. While I’ve seen many commentators call for Europe and the US to increase their graphite refinement capabilities, even going as far as to develop synthetic graphite, I would add that addressing the diverse energy needs of our evolving world will require a multitude of materials and technologies ; it’s unlikely that a perfect ‘’silver bullet’’ will overcome all challenges.

This is why it is also important to keep investing in alternative forms of battery #technology, such as sodium-ion batteries, solid state, silicon batteries or even hydrogen fuel cells.

The topic of retirement – and people’s readiness for it – plays an important role in investment and financial advice. As concerns grow over retirement savings, financial advice must adapt and change.

Retirement models around the world

Different countries have different vehicles for accumulation and decumulation of retirement investments.

The UK, for example, has:

• The state Pension,
• Workplace Pensions,
• and Personal Pensions.

Australia has the Superannuation Guarantee. Meanwhile IRAs & 401(k)s are the primary vehicle in the USA.

All plans evolve over the years, and much government legislation has been enacted to strengthen them. One such plan which has recently been introduced in the USA is the SECURE 2.0 Act. Passed by Congress in December 2022, it is an expansion of the Setting Every Community Up for Retirement Act, 2019 (SECURE Act).

A game-changer in the American landscape

The Act was introduced amid growing concerns about retirement security, influenced by global issues and lasting economic challenges. As a recent US News Survey of 2000 adults points out “50% of respondents say they had to pause saving for retirement at some point in 2022, and 41% of those surveyed stopped contributing to retirement funds like 401(k)s or individual retirement accounts.”

A recent poll, conducted by YouGov for Bankrate, found that over half of Americans feel they’re lagging in retirement savings, and almost half don’t have any retirement plan. This is especially true for small business employees. One of the main goals behind the SECURE 2.0 Act was to target these concerns.

It is important to remember that a significant percentage of this population is going to be the beneficiary of the Great Wealth Transfer, estimated to be close to $72 trillion. Wealth firms, especially those advising Mass Affluents and High Net Worth Gen Zs and Millennials, must adjust their strategies keeping in mind some of the important changes brought by the SECURE 2.0 Act.

Unlocking benefits for all ages

The Act addresses a range of age groups. On one hand, it allows for higher catch-up amounts of $10,000 for employees aged 60-63. At the same time, it treats student debt repayments as contributions to a 401(k) and enables transfers from certain 529 accounts to Roth IRAs.

Addressing student loan payments is, as the official Senate paper on SECURE 2.0 puts it, a significant benefit for young adults who are only just embarking on the retirement savings journey. The act allows for more tax optimized funds to become available for both accumulation and decumulation cycles.     

A greater role of financial advisors in the SECURE era

Under all these circumstances, the role of Financial Advisors becomes more and more important. As the spectrum of Wealth Management expands to the Mass Affluent segment, maximizing the benefits of SECURE 2.0 becomes a key part of advice. As a Vanguard article on the subject points out, “starting in 2024, employers will have the option to make contributions to an employer-sponsored retirement plan—i.e., 401(k), 403(b), SIMPLE IRA, 457(b), and similar plans—that match an employee’s qualified student loan payments, even if the worker doesn’t directly contribute to the retirement plan.” It is important for Financial Advisors to be aware of these changes, to help their clients optimize their retirement savings journey.

Small businesses, big benefits

SECURE 2.0 requires businesses to auto-enroll employees in new (k) and 403(b) plans. From 2025, these plans will start with a minimum 3% contribution rate. There is also the provision for transferring employees’ retirement plans, especially low-balance ones, when switching jobs. This is vital for low-balance savers, often found in smaller enterprises.

JP Morgan Asset Management’s 11^th Annual Guide to Retirement highlights the Act’s incentives for small businesses to offer retirement plans via tax credits. This targets nearly 50% of private sector employees working for in such establishments with no such plans. Those with employer-sponsored setups are more likely to save towards retirement by two-fold. It’s essential for Financial Advisors to be aware of this significant change and integrate it into their own advice.

Conclusion

The SECURE 2.0 act brings significant changes that should encourage retirement savings for Americans and will inevitably become an important part of all financial advice conversations. Wealth firms must ready themselves to quickly adopt these changes so they can remain relevant and continue to be the advisor of choice for their clients. 

Some days you get these chains of interesting events following up on each other.

This morning I read the “GPT can solve math[.. ]” paper (https://lnkd.in/dzd7K3sx), then I read some responses to that (o.a. Gary Marcus, X-tweets etc). During and after the TED AI I had many interesting discussions on the topic of probabilistic modelling vs models of math as we know (knew?) it, and this paper sparked some thoughts (so: mission accomplished).

It occurs to me that we have a generation of PhD students building LLMs that have probably not really got interested model thinking and mathematical proofs. I.e. the thinking behind Einsteins´ relativity theory, the thinking behind Euler’s Graph theory, the type of thinking that led (indeed) to a mathematical model that you can implement in a calculator (low footprint), calculates correctly (100% trustworthy) and in addition can calculate things 100% correct on input unseen before.

The question really condenses down to the fact whether you believe in the abstraction capability in current algorithms used for training todays LLMs. Are attention layers at all able to build abstractions on their own (and not regurgitating from abstractions it got ready-served by humans)? Optimism in the Valley is big, just add more data and the problem will go away.

But without changing the underlying attention layer design this seems to be a fallacy. Learning to abstract really means to build metalevels on your information, condense signals and their relations. That is something different as predicting chains of tokens. Such an abstraction layer can be seen as building a 3D puzzle, whereas current attention mechanisms seem single-layered. With a single layer, the most you can build is a 2D puzzle.

With that picture in mind, you can observe that current solutions by LLM suppliers is to extend the 2D puzzle, making it larger (adding more data from all over), or giving it higher resolution for a specific task (like the math paper mentioned above). But no sincere attempts seem to have been made yet to build a 3D picture, which would mean to rock the foundation of the foundation models and rebuild the attention layer mechanism to cover for this deficit.

Until then, let’s focus on getting functions to work reliable and off-load model-based tasks (math, engineering, logic, reasoning) to external capability agents and stop pretending that 2D can become 3D without changing the foundation.

Welcome to the brave new world of data, a world that is not just evolving but also actively being reshaped by remarkable technologies. It is a realm where our traditional understanding of data is continuously being challenged and transformed, paving the way for revolutionary methodologies and innovative tools.

Among these cutting-edge technologies, two stand out for their potential to dramatically redefine our data-driven future: generative AI and synthetic data.

In this article, we will delve deeper into these fascinating concepts.

We will explore what generative AI and synthetic data are, how they interact, and, most importantly, how they are changing the data landscape.

So, strap in and get ready for a tour into the future of data!

Understanding generative AI and synthetic data

Generative AI refers to a subset of artificial intelligence, particularly machine learning, that uses algorithms like generative adversarial networks (GANs) to create new content. It’s “generative” because it can generate something new and unique from random noise or existing data inputs, whether that be an image, a piece of text, data, or even music.

GANs are powerful algorithms that comprise two neural networks — the generator, which produces new data instances, and the discriminator, which evaluates them for authenticity. Over time, the generator learns to create more realistic outputs.

Today, the capabilities of generative AI have evolved significantly, with models like OpenAI’s GPT-4 showcasing a staggering potential to create human-like text. The technology is being refined and optimized continuously, making the outputs increasingly indistinguishable from real-world data.

Synthetic data refers to artificially created information that mimics the characteristics of real-world data but does not directly correspond to real-world events. It is generated via algorithms or simulations, effectively bypassing the need for traditional data collection methods.

In our increasingly data-driven world, the demand for high-quality, diverse, and privacy-compliant data is soaring.

Current challenges with real data

Across industries, companies are grappling with data-related challenges that prevent them from unlocking the full potential of artificial intelligence (AI) solutions.

These hurdles can be traced to various factors, including regulatory constraints, sensitivity of data, financial implications, and data scarcity.

1. Regulations:

Data regulations have placed strict rules on data usage, demanding transparency in data processing. These regulations are in place to protect the privacy of individuals, but they can significantly limit the types and quantities of data available for developing AI systems.

• Sensitive data:

Moreover, many AI applications involve customer data, which is inherently sensitive. The use of production data poses significant privacy risks and requires careful anonymization, which can be a complex and costly process.

• Financial implications:

Financial implications add another layer of complexity. Non-compliance with regulations can lead to severe penalties.

• Data availability:

Furthermore, AI models typically require vast amounts of high-quality, historical data for training. However, such data is often hard to come by, posing a challenge in developing robust AI models.

This is where synthetic data comes in.

Synthetic data can be used to generate rich, diverse datasets that resemble real-world data but do not contain any personal information, thus mitigating any compliance risks. Additionally, synthetic data can be created on-demand, solving the problem of data scarcity and allowing for more robust AI model training.

By leveraging synthetic data, companies can navigate the data-related challenges and unlock the full potential of AI.

What is synthetic data?

Synthetic data refers to data that’s artificially generated rather than collected from real-world events. It’s a product of advanced deep learning models, which can create a wide range of data types, from images and text to complex tabular data.

Synthetic data aims to mimic the characteristics and relationships inherent in real data, but without any direct linkage to actual events or individuals.

A synthetic data generating solution can be a game-changer for complex AI models, which typically require massive volumes of data for training. These models can be “fed” with synthetically generated data, thereby accelerating their development process and enhancing their performance.

One of the key features of synthetic data is its inherent anonymization.

Because it’s not derived from real individuals or events, it doesn’t contain any personally identifiable information (PII). This makes it a powerful tool for data-related tasks where privacy and confidentiality are paramount.

As such, it can help companies navigate stringent data protection regulations, such as GDPR, by providing a rich, diverse, and compliant data source for various purposes.

In essence, synthetic data can be seen as a powerful catalyst for advanced AI model development, offering a privacy-friendly, versatile, and abundant alternative to traditional data.

Its generation and use have the potential to redefine the data landscape across industries.

Synthetic data use cases:

Synthetic data finds significant utility across various industries due to its ability to replicate real-world data characteristics while maintaining privacy.

Here are a few key use cases.

Testing and development:

In testing and development, synthetic data can generate production-like data for testing purposes. This enables developers to validate applications under conditions that closely mimic real-world operations.

Furthermore, synthetic data can be used to create testing datasets for machine learning models, accelerating the quality assurance process by providing diverse and scalable data without any privacy concerns.

Healthcare:

The health sector also reaps benefits from synthetic data. For instance, synthetic medical records or claims can be generated for research purposes, boosting AI capabilities without violating patient confidentiality.

Similarly, synthetic CT/MRI scans can be created to train and refine machine learning models, ultimately improving diagnostic accuracy.

Financial services:

Financial services can utilize synthetic data to anonymize sensitive client data, allowing for secure development and testing.

Moreover, synthetic data can be used to enhance scarce fraud detection datasets, improving the performance of detection algorithms.

Insurance:

In insurance, synthetic data can be used to generate artificial claims data. This can help in modeling various risk scenarios and aid in creating more accurate and fair policies, while keeping the actual claimant’s data private.

These use cases are just the tip of the iceberg, demonstrating the transformative potential of synthetic data across industries.

Conclusion

In conclusion, the dynamic duo of generative AI and synthetic data is set to transform the data landscape as we know it.

As we’ve seen, these technologies address critical issues, ranging from data scarcity and privacy concerns to regulatory compliance, thereby unlocking new potential for AI development.

The future of synthetic data is promising, with an ever-expanding range of applications across industries. Its ability to provide an abundant, diverse, and privacy-compliant data source could be the key to unlocking revolutionary AI solutions and propelling us toward a more data-driven future.

As we continue to explore the depths of these transformative technologies, we encourage you to delve deeper and stay informed about the latest advancements.

Remember, understanding and embracing these changes today will equip us for the data-driven challenges and opportunities of tomorrow.

Here’s the last PoV from Capgemini that I co-authored with my colleague Isabelle Lamothe addressing how immersive and metaverse experiences can revolutionize the future of learning and training.

Against a backdrop of transformation in our relationship with work, companies today must continually adapt to gain or maintain their competitive edge. Today, 77% employers find it difficult to recruit the right people while 60% of workers will require training before 2027.

If we talk about employee experience, we cannot ignore the major pillar that is what we call “future of learning”. And a lot of work is required to transform the companies in the right direction.

This objective can be accelerated by using technologies to empower our ways to train and learn. And we are deeply convinced that immersive experiences, like metaverse, are a main lever to reach the next level of learning.

Today, many organizations continue to innovate through the metaverse and immersive experiences and curiosity about the metaverse remains high, especially when it comes to training, as shown by a study carried out by Capgemini Research Institute. 61% of respondents believe that immersive experiences can have an impact on the training sector. We still have some time to go before the metaverse reaches full maturity, but it opens a wide field of actionable possibilities right now.

At Capgemini, we aim at becoming a strong partner for our clients by accompanying them through their will to put their training ecosystem to the next step.

Let’s open discussion if you’re interested in this.

Read the PoV

Point of view

The future of learning is immersive

In the fast-paced world of banking, customer service plays a pivotal role in building trust and loyalty.

Contact center agents are the frontline champions responsible for addressing customer queries, resolving issues, and providing essential assistance. However, with the increasing complexity of financial products and the growing demands of customers, contact center agents often face challenges in providing accurate and timely information. This is where generative AI emerges as a game-changer, empowering agents with the right tools to enhance their performance and deliver a seamless customer experience.

Generative AI in banking

Generative AI, a groundbreaking technology, is making significant strides in the banking sector. By harnessing the power of advanced language models, it allows banks to streamline customer service operations, automate routine tasks, and provide personalized interactions. From generating tailored responses to enhancing fraud detection and risk assessment, Generative AI is transforming the way banks interact with customers and optimize their internal processes, ultimately leading to improved efficiency and customer satisfaction.

Use cases of generative AI in banking contact centers

1. Abstract summarization: Abstract summarization is a cutting-edge approach implemented in contact centers to empower agents and streamline their workflow. The process begins with the contact center system recording and transcribing customer calls accurately. Utilizing the power of GPT-3, a robust language model, the system then generates concise summaries by extracting essential information from the conversations. Later, they are presented to the agents, allowing them to quickly review and approve the condensed call transcripts.
2. Insight extraction: Harnessing the power of Generative AI, contact centers can extract valuable business insights from vast conversation-related datasets. By analyzing the reasons behind customer calls in conjunction with average handle time, average hold time, and average queue time, agents become truly empowered. These insights provide a deeper understanding of call patterns, enabling agents to optimize their performance and improve the overall efficiency.
3. Real time digital assistant: Generative AI can be used to assist agents in handling customer inquiries more efficiently. By granting access to a vast knowledge base, the AI model efficiently retrieves relevant information and proposes suitable responses for agents. This capability empowers them to deliver accurate and consistent answers to customer queries, resulting in enhanced satisfaction and a more seamless customer service experience.
4. Complaint resolution: Resolving customer complaints is crucial for maintaining a positive brand image and delivering a high-quality experience. Generative AI can analyze historical data on successful complaint resolutions and recommend the most effective strategies to agents. This ensures that agents have the necessary tools to address customer grievances promptly and effectively, while also providing a superior customer experience.
5. Personalized customer interaction: Generative AI can empower agents to provide personalized customer interaction and tailored responses to customers, thereby enhancing their overall experience. By analyzing customer data, Gen AI can generate customized responses that cater to specific needs, fostering meaningful and impactful interactions that make customers feel valued and understood.
6. Automated ticket routing: Some queries might require specialized assistance or escalation to the higher-level support. Generative AI can analyze incoming queries and route them to the appropriate departments, ensuring a streamlined workflow and reducing response times.
7. Automated email response: By analyzing vast amounts of customer data, Gen AI can generate personalized and contextually appropriate email replies, tailored to each customer’s specific inquiry. This technology streamlines the response process, saving agents valuable time and effort, while ensuring customers receive timely and accurate solutions, ultimately enhancing overall customer service and experience.

Benefits of Generative AI for agents:

1. Enhanced efficiency: By automating repetitive tasks and providing quick access to information, Generative AI significantly reduces the average handling time for customer inquiries. Agents can focus on more complex issues and provide a higher level of service.
2. Consistency and accuracy: Generative AI ensures consistent and accurate responses across all customer interactions. This consistency builds trust and eliminates the risk of misinformation.
3. Continuous learning: AI models learn from every customer interaction, making them more knowledgeable and better equipped to handle future queries effectively.
4. Improved job satisfaction: Empowered with Generative AI tools, agents can handle customer inquiries with confidence and efficiency, leading to higher job satisfaction and lower agent turnover.
5. Cost savings: Automating routine tasks and optimizing agent performance results in cost savings for banks, as they can handle more queries with the same staff strength.

Conclusion

Generative AI is revolutionizing the banking industry by empowering contact center agents to deliver exceptional customer service. By providing quick access to information, personalized customer interactions, and compliance support, Generative AI enhances agent performance and customer satisfaction. As banks continue to adopt this technology, they will undoubtedly witness significant improvements in their contact center operations, ultimately leading to a more positive and seamless customer experience.

Implementing frictionless, digitally augmented, and data-driven deal desk operations can drive operational excellence and increased value across the sales lifecycle.

Just as a rally driver relies on a navigator to get them from A to B so they can stay focused on the speed and agility needed to win the race, so the sales function needs an “intuitive deal desk” to optimize deal value while mitigating risk.

But what – you might ask – is a “deal desk”? And how can it be made “intuitive”?

A deal desk is a cross-functional team that analyzes, tracks, converts to quotes, reviews, and approves sales opportunities in real time, so they can focus more on speed to sales. This supports the strategic revenue and profitability goals of the sales strategy and provides rigor and structure to the tactical and dynamic realities of negotiation, approvals, and handling non-standard attributes of supporting a deal across the sales lifecycle.

However, a deal desk operation frequently comes up against a range of challenges that include:

• Insufficient, inaccurate, and inconsistent data
• Lack of insights into sales drivers
• Lack of visibility into pricing analytics
• Outdated sales technology.

These challenges often result in inefficient and non-intuitive deal desk operations.

In turn, this can lead to unnecessary time and effort spent dealing with day-to-day tactical issues and losing sight of strategic levers such as win-rate drivers, improving margins, and stopping revenue leakage.

Leverage intelligent, frictionless deal desk operations

What’s needed is a smart, seamless deal desk operations model that can be tailored to the culture, practices, and needs of the individual organization – and that empowers the people who use it.

At Capgemini, we recommend implementing a “Deal Life Cycle Cockpit” (Figure 1) that leverages three key elements:

• Deal input – information on the deal helps establish accountability across the deal sales lifecycle.
• Deal optimization – a deal review interface that automates analysis, expands configuration capabilities, and helps to make sense of optimization recommendations and how they are adopted by sales personnel.
• Deal review – integrating recommendations (from data science and pricing analytics) on key deal optimization techniques support business outcomes and, in turn, the bottom-line.

Technology eliminates process drag and improves how internal and external users experience it. This not only delivers hard gains (ROI, revenue gains and incremental margin), but also qualitative benefits such as improved CSAT/NPS that translates to stickiness, repurchase, loyalty, and “mindshare.” The integration of rich data and extended sales and pricing analytics delivers incremental business gains.

Accelerating deal approval and win rates

Capgemini partners with our clients to reimagine their deal desk operations model – integrating, streamlining, and optimizing sales touchpoints across the lead-to-order lifecycle through leveraging our tested process, data, technology, and outcome-driven approach.

Enriching our clients’ digital sales strategy with data-driven insights and operational excellence across the sales function has led to some truly transformative business outcomes, including:

• 20–40% decrease in deal approval turnaround time
• 3–5% increase in win rates
• 20–30% increase in customer satisfaction
• 35–45% reduction in costs, and
• 15–25% reduction in escalations.

To prevent you from crashing into a ditch and getting covered in mud, intuitive deal desk operations help you drive operational excellence and increased value across your organization’s sales lifecycle.

To learn how Capgemini’s Empowered Sales Operations solution can help you in unleashing competitive advantage from your deal desk operations for increased sales and user experience, contact: deepak.bhootra@capgemini.com.

Every generation of wireless technology has required organizations to adapt their security practices to effectively monitor and protect their networks. But monitoring a 5G network presents a new level of complexity due to the different protocols and architecture involved.

In our final blog of the 5G security series, it’s time to explore the complexities of monitoring a 5G network and how organizations can ensure that their infrastructure is watertight.

The 5G step change

Traditionally, security monitoring has focused on IT networks, such as MPLS or IP networks, where most security operations centers (SOCs) operate from. These SOCs primarily monitor enterprise systems like office, financial, and HR systems. However, with the proliferation of connectivity in operational environments, including manufacturing facilities, warehouses, and critical infrastructure, the monitoring scope has evolved to include operational technology (OT) networks too.

OT networks differ from enterprise networks in terms of protocols and tools required for monitoring. Proprietary protocols often govern devices and equipment in the OT environment, each requiring specific protocols and tools for monitoring.

Unlike IP networks, 5G networks operate on cellular protocols and follow cellular standards developed over previous generations (e.g., 2G, 3G, 4G). The difference is that as organizations deploy their own private or hybrid 5G networks, the responsibility for monitoring these networks shifts from telco providers to the enterprises themselves.

This is a completely new world for organizations, introducing unique complexities tied to cellular protocols and the division between the control and data plane (the former handles the initial handshake, authentication, encryption, and bandwidth allocation, while the latter facilitates the actual data transfer). Monitoring both planes and correlating the data is essential for effective 5G network security operations.

24×7 log collection

A fundamental aspect of 5G network security is continuous monitoring through 24×7 log collection. Logs are gathered from various components spanning from the user equipment (UE) to the core, providing crucial insights into potential security events.

The extent of log collection depends on the deployment model adopted. In private deployment models, higher volumes of log collection is possible. However, where the 5G architecture is shared with mobile network operators (MNOs), the service provider must collaborate to ensure the necessary logs are collected.

To achieve comprehensive monitoring, it is essential to collect logs from both the control plane and the data plane of the 5G architecture. Additionally, specialized toolsets are required as existing enterprise log collection tools may not fully comprehend the specific protocols, such as GTP, used in 5G networks. These tools not only collect data but also correlate them to identify ongoing attacks effectively.

Indicators of compromise

The next aspect of monitoring are indicators of compromise(IoCs),which play a vital role in detecting security attacks within the 5G environment. The best-in-class toolsets available today provide a range of IoCs that can be utilized by SOC analysts to identify potential security breaches. These IoCs can subsequently be categorized into device-related and traffic/performance-related indicators.

Some examples of device related IoCs include:

Detecting unknown devices in the network, Monitoring changes in device connection status, Identifying new device vendors, Detecting devices that have not been seen for a specific period, Identifying new device types, Monitoring abnormal device traffic usage, Tracking abnormal traffic usage by devices in specific locations, Identifying user equipment (UE) connection failures, Detecting consistent failures in UE IP allocation, Identifying conflicting IMEI numbers with SUPI and SUCI mapping, Detecting unknown UEs joining the network, Monitoring repeated UE authorization failures, Identifying devices with unknown locations, Identifying devices with vulnerabilities or performance issues.

Similarly some traffic and performance IoCs include

Identifying unauthorized traffic patterns, Monitoring compliance with quality of service (QoS) parameters, Detecting abnormal traffic for specific devices or applications, Monitoring the absence of traffic, Identifying abnormal protocol usage for user equipment (UE) and Internet of Things (IoT) devices, Detecting spikes in control traffic to UE, radio access network (RAN), and core, Monitoring spikes in user plane data, potentially indicating distributed denial of service (DDoS) attacks

These IoC examples offer a glimpse into the extensive use cases built around them, and with the right tools, SOC analysts should feel empowered to detect and respond to security breaches effectively.

Best practices for securely monitoring 5G networks

Monitoring a cellular network can be complex but when taken step-by-step it can also be manageable and efficient:

• Develop expertise: Invest in training and familiarize the team with the unique aspects of 5G protocols and the control-data plane division.
• Collaborate with telco providers: Engage with telco providers to understand their monitoring capabilities and coordinate efforts to ensure end-to-end security for private 5G networks.
• Adopt specialized tools: Acquire monitoring tools designed specifically for 5G networks, capable of monitoring both the control plane and the data plane. These tools should provide comprehensive visibility and correlation capabilities.
• Implement network slicing: Leverage network slicing capabilities to isolate and monitor different slices within the 5G network. This approach enhances security and allows focused monitoring for specific services or devices.
• Continuous monitoring and analysis: Implement real-time monitoring and analysis to identify anomalies, detect potential threats, and respond promptly. Incorporate threat intelligence feeds to stay updated on emerging threats and vulnerabilities in 5G networks.

As these different components come together in different deployment models, achieving end-to-end security in 5G can become challenging. This is why IT, OT, and cellular network security policies must all be well aligned and integrated to bring enterprise grade security that is governed by zero trust principle protecting north–south and east–west traffic as well as data at storage and in transit. 

Overall, any monitoring of a 5G network requires organizations to adapt their security practices to the unique characteristics of cellular protocols and the control-data plane division. By investing in expertise, collaborating with telco providers, leveraging specialized tools, and adopting best practices, organizations can ensure the security of their 5G networks and start embracing the benefits of 5G technology.

Contact Capgemini today to find out about 5G security.