Showing posts with label AI. Show all posts
Showing posts with label AI. Show all posts

Tuesday, 20 January 2026

Telecom Security Realities from 2025 and Lessons for 2026

Telecom security rarely stands still. Each year brings new technologies, new attack paths, and new operational realities. Yet 2025 was not defined by dramatic new exploits or spectacular network failures. Instead, it became a year that highlighted how persistent, patient and methodical modern telecom attackers have become.

The recent SecurityGen Year-End Telecom Security Webinar offered a detailed look back at what the industry experienced during 2025. The session pulled together research findings, real world incidents and practical lessons from across multiple domains, including legacy signalling, eSIM ecosystems, VoLTE vulnerabilities and the emerging world of satellite-based mobile connectivity.

For anyone working in mobile networks, the message was clear. The threats are evolving, but many of the core problems remain stubbornly familiar.

A Year of Stealth Rather Than Spectacle

One of the most important themes from the webinar was that 2025 did not bring a wave of highly visible disruptive telecom attacks. Instead, it was characterised by quiet, low profile intrusions that often went undetected for long periods.

Operators around the world reported that attackers increasingly favoured living-off-the-land techniques. Rather than deploying noisy malware, intruders looked for ways to gain legitimate access to core systems and remain hidden. Lawful interception platforms, subscriber databases such as HLR and HSS, and internal management platforms were all targeted.

The primary objective in many cases was intelligence collection. Attackers were interested in call data, subscriber information and network topology rather than immediate disruption. This shift in motivation makes detection far more difficult, as there are often few obvious signs of compromise.

At the same time, automation has become a defining feature on both sides of the security battle. Operators are investing heavily in AI and machine learning to identify abnormal behaviour. Attackers are doing exactly the same, using automation to scale phishing campaigns and to accelerate exploit development.

Despite all this technology, basic security discipline continues to be a major challenge. A significant proportion of incidents still originate from human error, poor operational practices or simple failure to apply patches. The industry continues to invest billions in cybersecurity, but much of that effort is consumed by reporting and compliance activities rather than direct threat mitigation.

eSIM Security Comes into Sharp Focus

The transition from physical SIM cards to eSIM and remote provisioning is one of the most significant structural changes in the mobile industry. It offers clear benefits in terms of flexibility and user experience. However, the webinar highlighted that it also introduces entirely new security concerns.

Traditional SIM security models relied heavily on physical control. Fraudsters needed access to large numbers of real SIM cards to operate at scale. With eSIM, many of those physical constraints disappear. Remote provisioning expands the number of parties involved in the connectivity chain, including resellers and intermediaries who may not always operate under strict regulatory oversight.

During 2025 several major SIM farm operations were dismantled by law enforcement. These infrastructures contained tens of thousands of active SIM cards and were used for large scale fraud, smishing campaigns and automated account creation. While such operations existed long before eSIM, the technology has the potential to make them even easier to deploy and manage.

Research discussed in the session pointed to additional concerns. Analysis of travel eSIM services revealed issues such as cross-border routing of management traffic, excessive levels of control granted to resellers, and lifecycle management weaknesses that could potentially be abused by attackers. In some cases, resellers were found to have capabilities similar to full mobile operators, but without equivalent governance or transparency.

The conclusion was not that eSIM is inherently insecure. The technology itself uses strong encryption and robust mechanisms. The problem lies in the wider ecosystem of trust boundaries, partners and processes that surround it. Securing eSIM therefore requires cooperation between operators, vendors, regulators and service providers.

SS7 Remains a Persistent Weak Point

Few topics in telecom security generate as much ongoing concern as SS7. Despite being a technology from a previous era, it remains deeply embedded in global mobile infrastructure. The webinar dedicated significant attention to why SS7 continues to be exploited in 2025 and why it is likely to remain a problem for many years to come.

Throughout the year, media reports and research papers continued to demonstrate practical abuses of SS7 signalling. Attackers probed networks, attempted to bypass signalling firewalls and looked for new ways to manipulate protocol behaviour. Techniques such as parameter manipulation and protocol parsing tricks were highlighted as methods that can sometimes evade existing protections.

One particularly interesting demonstration showed how SS7 messages could be used as a covert channel for data exfiltration. By embedding information inside otherwise legitimate signalling transactions, attackers can potentially move data across networks without triggering traditional security alarms.

Perhaps the most striking point raised was how little progress has been made in eliminating SS7 dependencies. Analysis of global network deployments showed that only a handful of countries operate mobile networks entirely without SS7. Everywhere else, the protocol remains a foundational element of roaming and interconnect.

As a result, even operators that have invested heavily in 4G and 5G security can still be undermined by weaknesses in this legacy layer. The uncomfortable reality is that SS7 vulnerabilities will continue to be exploited well into 2026 and beyond.

VoLTE and Modern Core Network Risks

While legacy protocols remain a problem, modern technologies are not immune. VoLTE infrastructure in particular was identified as an increasingly attractive target.

VoLTE relies on complex interactions between signalling systems, IP multimedia subsystems and subscriber databases. Weaknesses in configuration or interconnection can open the door to call interception, fraud or denial of service. Several real world incidents during 2025 demonstrated that attackers are actively exploring these paths.

The move toward fully virtualised and cloud-native mobile cores also introduces new operational challenges. Telecom networks now resemble large IT environments, complete with the same risks around misconfiguration, insecure APIs and exposed management interfaces.

The Emerging Security Challenge of 5G Satellites

One of the most forward-looking parts of the webinar focused on non-terrestrial networks and direct-to-device satellite connectivity. What was once a concept for the distant future is rapidly becoming a commercial reality.

Satellite integration promises to extend 5G coverage to remote areas, oceans and disaster zones. However, it also changes the security model in fundamental ways. Satellites can act either as simple relay systems or as active components of the mobile radio access network. In both cases, new threat vectors emerge.

Potential issues discussed included the risk of denial of service against shared satellite resources, difficulties in applying traditional radio security controls in space-based equipment, and the possibility of more precise user tracking due to the way satellite systems handle location information.

Experts from the space cybersecurity community explained how vulnerabilities in mission control software and ground segment infrastructure could be exploited. Much of this software was originally designed for isolated environments and is only now being connected to wider networks and the internet.

As telecom networks expand beyond the boundaries of the Earth, security responsibilities extend with them. Operators will need to think not only about terrestrial threats but also about risks originating from space-based components.

The Human Factor and the Skills Gap

Technology was only part of the story. Another recurring theme was the global shortage of skilled telecom cybersecurity professionals.

Studies referenced in the session suggested that millions of additional specialists are needed worldwide, yet only a fraction of that demand can currently be filled. Many security teams are overwhelmed by the sheer volume of alerts and data they must process.

This shortage has real consequences. When teams are stretched thin, patching is delayed, anomalies are missed and complex investigations become difficult to sustain. The panel emphasised that throwing more tools at the problem is not enough. Organisations must focus on training, automation and smarter operational processes.

Automation and AI-driven analysis were presented as essential enablers. Given the scale of modern mobile networks, it is simply not feasible for human analysts to monitor every signalling protocol, every core interface and every emerging technology manually.

Preparing for 2026

Looking ahead, the experts agreed on several broad trends. Attacks on legacy systems such as SS7 will continue. Fraudsters will increasingly target eSIM provisioning processes. VoLTE and 5G core components will face growing scrutiny. Satellite-based connectivity will introduce new and unfamiliar security questions.

Perhaps most importantly, the line between traditional telecom security and general cybersecurity will continue to blur. Mobile networks are now large, distributed IT platforms, and they inherit all the complexities that come with that transformation.

Operators, regulators and vendors must therefore adopt a holistic view. Investment must go beyond compliance reporting and focus on practical defences, real time monitoring and collaborative intelligence sharing.

Final Reflections

The SecurityGen webinar provided a valuable snapshot of an industry at a crossroads. Telecom networks are becoming more advanced and more capable, but also more complex and interconnected than ever before.

2025 demonstrated that attackers do not always need new vulnerabilities. Often they succeed simply by exploiting old weaknesses in smarter ways. The challenge for 2026 is to close those gaps while also preparing for the technologies that are only just beginning to emerge.

For those involved in telecom security, the full discussion is well worth watching. The complete webinar recording can be viewed below:

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Tuesday, 10 June 2025

Cloud Native Telco Transformation Insights from T-Systems

The journey to becoming a cloud-native telco is not just a buzzword exercise. It requires a full-scale transformation of networks, business models, and operating cultures. At Mobile Europe’s Becoming a Cloud-Native Telco virtual event, Richard Simon, CTO at T-Systems International, outlined how telcos are grappling with this change, sharing insights from both successes and ongoing challenges.

Cloud-native is not just about adopting containers or orchestrating with Kubernetes. Richard Simon described it as a maturity model that demands strategic vision, architectural readiness, and cultural shift. The telco industry, long rooted in proprietary systems, is gradually moving towards software-defined infrastructure. Network Function Virtualisation (NFV) remains foundational, enabling operators to decouple traditional monolithic services and deliver them in a modular, digital-native manner—whether in private data centres or public clouds.

The industry is also seeing the rise of platform engineering. This evolution builds on DevOps and site reliability engineering (SRE) to create standardised internal developer platforms. These platforms reduce cognitive load for developers, increase consistency in toolchains and workflows, and enable a shift-left approach for operations and security. It is a critical step towards making innovation scalable and repeatable within telcos.

The cloud-native ecosystem has exploded in scope, with the CNCF landscape illustrating the diversity and maturity of open source components now in use. Telcos, once cautious about community-led projects, are now not only consuming but also contributing to open source. This openness is pivotal for achieving agility and interoperability in a multivendor environment.

With the increasing complexity of hybrid and multicloud strategies, avoiding vendor lock-in has become essential. Richard highlighted how telcos are optimising costs, improving resilience, and aligning workloads with the most suitable cloud environments. Multicloud is no longer a theoretical construct. It is operational reality. But with it comes the need for new thinking around cloud economics.

Cloud financial operations are no longer limited to cost tracking. They now include strategic frameworks (FinOps), real-time cost management, and a growing focus on application profiling. Profiling looks at how software consumes cloud resources, guiding developers to write more efficient code and enabling cost-effective deployments.

While generative AI dominates headlines, the pace of adoption in telco is deliberate. Richard pointed out that while investment in GenAI is widespread, only a small fraction of deployments have reached production. Most telcos are still in proof-of-concept or pilot phases, reflecting the technical and regulatory complexity involved.

Unlike some sectors, telcos face strict compliance requirements. GenAI inference stages—when customer data is processed—raise concerns around data sovereignty and privacy. As a result, telcos are exploring how to balance innovation with responsibility. Some are experimenting with fine-tuning foundational models internally, while others prefer to consume GenAI as a service, depending on use cases ranging from network automation to document processing.

GenAI is a high-performance computing (HPC) workload. Training large models requires significant infrastructure, making decisions around build versus buy critical. Richard outlined three tiers of AI adoption: infrastructure-as-a-service for DIY approaches, foundation-model-as-a-service for fine-tuning, and software-as-a-service for fully hosted solutions. Each tier comes with trade-offs in control, cost, and complexity.

Looking ahead, three themes stood out in Richard’s conclusions.

First, the era of AI agents is beginning. These autonomous systems, capable of reasoning and acting across complex tasks, will be the next experimentation frontier. Pilots in 2025 and 2026 will pave the way for a broader agentic AI economy.

Second, cloud economics continues to evolve. Operators must invest in cost visibility and governance rather than reactively scaling back cloud usage. The emergence of profiling and observability tooling is helping align cost with performance and business value.

Third, sovereignty is rising in importance. Telcos must ensure control over data and infrastructure, not only to comply with regional regulations but also to maintain intellectual property and operational resilience. Sovereign cloud models, abstracted control planes, and localised inference infrastructure are becoming strategic imperatives.

His complete talk is embedded below:

The cloud-native journey is not linear. It requires operators to architect for modularity, align with open ecosystems, and stay grounded in real-world economics. As Richard Simon’s keynote showed, the transformation is well underway, but its success will depend on how telcos integrate cloud, AI, and sovereignty into a coherent and adaptable strategy.

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Wednesday, 26 February 2025

Reigniting Growth in the Telecom Industry with AI and Cloud

The telecom industry is at a crossroads. While demand for connectivity continues to surge, operators face stagnating revenues, rising costs, and increasing competition. In his keynote at the Brooklyn 6G Summit 2024, Manish Singh, CTO of Telecom Systems Business at Dell Technologies, outlined a compelling vision for how AI and cloud-native networks can reignite growth in the sector.

The Growth Challenge in Telecom

The traditional telecom business model is under pressure. Operators are struggling with:

  • Revenue stagnation despite increasing data consumption.
  • Rising operational costs driven by legacy infrastructure and inefficient processes.
  • Intensifying competition from hyperscalers and alternative connectivity providers.

To overcome these challenges, Manish argues that telcos must embrace AI-native and cloud-native architectures as fundamental enablers of transformation.

AI: The Catalyst for Intelligent Networks

AI is not just an add-on; it must be at the core of future telecom networks. Manish highlighted several ways AI can drive growth:

  • Automation of network operations: AI-driven predictive maintenance and self-optimising networks reduce downtime and operational expenses.
  • Enhanced service delivery: AI enables hyper-personalised customer experiences and intelligent traffic management.
  • Operational efficiency: AI optimises energy consumption, spectrum allocation, and overall network resource utilisation.

Manish emphasised that AI-native networks will be a defining feature of 6G, making networks more autonomous, efficient, and scalable.

Cloud-native Architectures: The Foundation for Scalability

Moving beyond traditional, hardware-centric networks is essential. Manish advocates for a cloud-first approach, where telecom networks are:

  • Software-defined and virtualised, reducing dependence on costly proprietary hardware.
  • Highly scalable, allowing operators to adjust capacity dynamically.
  • Interoperable and open, fostering innovation through Open RAN and disaggregated networks.

By embracing cloud-native principles, telcos can accelerate service delivery, reduce costs, and stay competitive in an increasingly software-driven ecosystem.

AI Infrastructure: Scaling from Edge to Core

A key enabler of AI and cloud-native networks is the AI Factory approach, which provides scalable infrastructure from mega-scale data centres to the edge. Manish highlighted how AI workloads must be supported across different network layers—from on-premise enterprise deployments to far-edge, near-edge, and core data centres.

Dell Technologies' AI Factory is designed to:

  • Support diverse AI edge use cases in telecom.
  • Handle power and cooling constraints, crucial for efficient AI model training and inference.
  • Leverage cloud-native architectures to ensure seamless scalability and automation across the entire network.

This modular infrastructure ensures that telecom networks can efficiently process AI workloads at every layer, enabling real-time decision-making and optimised operations.

Overcoming Challenges in AI and Cloud Adoption

Despite the clear benefits, Manish acknowledged key barriers:

  • Legacy infrastructure: Transitioning from traditional networks requires significant investment.
  • Security and privacy concerns: AI-driven automation raises questions about data integrity and network security.
  • Industry mindset shift: Operators must adopt a culture of innovation and rapid iteration.

Addressing these challenges requires industry-wide collaboration, strong partnerships with cloud providers, and a commitment to open innovation.

Conclusion: The Time to Act is Now

Manish’s message to the industry was clear—AI and cloud are not future aspirations; they are essential for telecom survival and growth. By leveraging AI-native automation and cloud-native architectures, operators can reignite growth, drive efficiency, and prepare for the 6G era.

Watch Manish Singh’s full keynote embedded below:

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Friday, 17 January 2025

Lessons from ANRW ’24: AI and Cloud in 5G/6G Systems

The ACM, IRTF & ISOC Applied Networking Research Workshops (ANRW) offer a vibrant forum for researchers, vendors, network operators, and the Internet standards community to exchange emerging results in applied networking research. To foster collaboration across these diverse groups, ANRW events are co-located with IETF standards meetings, typically held annually in July. These workshops prioritise interactive discussions and engagement, complementing traditional paper presentations.

ANRW '24, held on 23 July 2024 at the Hyatt Regency Vancouver, brought together industry leaders and academics to share insights on advancing networking technologies. Among the standout sessions was a keynote presentation by Sharad Agarwal, Senior Principal Researcher at Microsoft. His keynote titled, "Lessons I Learned in Leveraging AI+ML for 5G/6G Systems", highlighted pivotal themes influencing telecom and networking.

Sharad distilled his experiences into three key lessons, each underscored by examples of research and systems developed to address specific challenges in the telecom industry:

  1. Leverage Cloud Scale to Overcome Limitations of Deployed Protocols: He emphasised that the scale of cloud computing is critical to managing the massive demands of modern telecom networks. For instance, systems like TIPSY (Traffic Ingress Prediction SYstem) demonstrate how AI and ML can predict traffic ingress points across thousands of peering links, helping to avoid bottlenecks and ensure optimal traffic distribution.
  2. Custom Learning Algorithms vs. Off-the-Shelf Solutions: While bespoke algorithms offer higher precision for niche applications, their complexity and deployment challenges often outweigh their benefits. Sharad argued for balancing innovation with practicality, advocating for leveraging pre-built AI and ML models wherever possible to streamline integration.
  3. Mitigate Risks of AI Hallucinations through Careful System Design: Acknowledging the risks posed by unreliable AI outputs, he stressed the importance of robust system design. Using LLexus, an AI-driven incident management system, as an example, Sharad highlighted techniques like iterative plan generation, validation rules, and human auditing as essential safeguards against AI errors.

The talk also delved into broader trends shaping the telecom landscape:

  • Cloudification of Telecom Infrastructure: The shift from hardware-based to software-based network functions, underpinned by cloud-native principles, has revolutionised telco infrastructure. This transformation facilitates rapid upgrades, reduces costs, and introduces new opportunities for AI-driven analytics.
  • Challenges in Performance and Reliability: Ensuring high throughput, low latency, and carrier-grade reliability in cloudified networks remains a significant hurdle. Innovations like PAINTER and LLexus demonstrate how AI and ML are being applied to optimise these aspects.
  • Emerging Business Models and Private Deployments: The integration of new radio technologies and virtualised network functions is driving novel revenue streams, such as private 5G/6G networks for mission-critical applications like factory automation.

Finally, Sharad’s keynote underscored how AI, ML, and cloud computing are reshaping the telecom industry, particularly in the era of 5G and the forthcoming 6G. By leveraging the scale of cloud infrastructure, balancing algorithmic complexity, and designing systems with resilience against AI pitfalls, the industry is poised to meet its ambitious goals of high bandwidth, low latency, and unparalleled reliability.

The video of his talk is embedded below and the slides are available here:

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Wednesday, 29 November 2023

AI/ML and Other ICT Industry Trends in the coming decades

At the Brooklyn 6G Summit (B6GS) 2023, top tier economist Dr. Jeff Shen from BlackRock, presented a talk from the industry perspective of AI (Artificial Intelligence) and investment. Jeff Shen, PhD, Managing Director, is Co-CIO and Co-Head of Systematic Active Equity (SAE) at BlackRock. He is a member of the BlackRock Global Operating Committee, BlackRock Systematic (BSYS) Management Committee and the BlackRock Asian Middle Eastern & Allies Network (AMP) Executive Committee.

In his talk he covered the history of how and where AI has been traditionally used and how the thinking around AI has changed over the last few decades. He then presented his view on if AI is just a fad or it's more than that. To illustrate the fact, he provided an example of how Generative AI market is expected to grow from $40 Billion in 2022 to $1.3 Trillion in 2032. 

There are many challenges that AI faces that one should be aware of; namely regulation, cyber threats and ethical concerns. In the US, AI touches the entire economy, from legal to healthcare. In their quarterly reporting, firms are now discussing AI and the larger tech companies are not afraid to grow inorganically in order to get more exposure to the trend. 

You can watch the whole of his talk embedded below, courtesy of IEEE Tv.

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Wednesday, 4 October 2023

Presentations from 2nd IEEE Open RAN Summit

The second IEEE SA (Standards Association) Open RAN summit, hosted by the Johns Hopkins University Applied Physics Lab, took place on 9-10 Aug 2023. It covered the topics related to the standardization of Open RAN including O-RAN Alliance, 3GPP, IEEE, various deployment scenarios, testing and integration, Open RAN security, RAN slicing, and RAN optimization among others. 

The videos of the presentations can be viewed on the summit page here or though the video playlist here.

The talk from Dr. Chih-Lin I, O-RAN Alliance TSC Co-Chair and CMCC Chief Scientist, Wireless Technologies on 'AI/ML impact, from 5.5G to 6G' is embedded below:

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Tuesday, 15 February 2022

What Is the Role of AI and ML in the Open RAN and 5G Future?

Artificial Intelligence and Machine Learning have moved on from just being buzzwords to bringing much needed optimization and intelligence in devices, networks and infrastructure; whether on site, on the edge or in the cloud.

Qualcomm has been very active in talking about AI/ML in webinars and on their site. A detailed blog post looking at 'What’s the role of artificial intelligence in the future of 5G and beyond?' is available here. It was posted in time for a Light Reading webinar where Gabriel Brown, Principal Analyst – Mobile Networks and 5G, Heavy Reading and Tingfang Ji, Senior Director, Engineering - Wireless R&D, Qualcomm discuss the topic. The video is embedded below and slide deck is available here.

Louis Scialabba, Senior Director of Marketing at Mavenir, looking at AI and Analytics spoke at Layer 123 conference on the topic, 'AI/ML for Next Gen 5G Mobile Networks'. His talk is embedded below and a blog post by him on the topic, 'The RIC Opens a New World of Opportunities for CSPs' is available here.

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Monday, 24 May 2021

ITU Standardization Bureau on Machine Learning for 5G


Last year I blogged about Global ITU AI/ML 5G Challenge on the theme “How to apply ITU's ML architecture in 5G networks".  The grand challenge finale happened in December. All the recording and presentations are available here.

Back in October, Bilel Jamoussi from ITU presented a keynote to the 2020 IEEE 5G World Forum plenary session where he addressed the challenges of applying machine learning in networks, ITU’s ML toolkit, and ITU’s AI/ML in 5G Competition. IEEE Tv shared the presentation only in April so the competition part is a bit outdated. It does nevertheless an interesting 20 minute talk.

ITU Recommendation Y.3174, Framework for data handling to enable machine learning in future networks including IMT-2020 is available here.

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Thursday, 13 May 2021

Anomaly Detection and other AI Algorithms in RAN Optimization


Yesterday I watched this very inspiring live chat that I would like to recommend to anyone who is interested in how machine learning techniques (aka "AI") can help to optimize and troubleshoot the Radio Access Network.

 [The real contents of in the video starts at approx. 42:00 min] 

My key takeaways from this fireside chat are: 

Verizon Wireless has enough data (100… 500 time series KPIs per cell) that they use to feed anomaly detection ML algorithms and this generates a huge number of alarms, but only a few actionable outputs. 

The “big elephant” (Nick Feamster) is to identify if these alarms indicating real problems that can and have to be fixed or if they just indicate a new behavior of e.g. a new handset or a SW version that was not present in the training phase of the ML algorithm and hence, its pattern is detected as a new “anomaly”. 

For Bryan Larish (Director Wireless AI Innovation, Verizon) the “big open problem” is “that it is not clear what the labels are” and “no standard training sets exist”. 

[For more details watch the video section between 52.00 min and 57:32 min and listen to Bryan’s experience!] 

In most cases Verizon seems to need subject matter experts to classify and label these anomaly alarms due to “the huge diversity” in data pattern. 

According to Bryan only for very few selected use cases it is possible to build an automated loop to fix the issue. Especially the root causes of radio interference are often mechanical or cabling issues that need manual work to get fixed. 

All in all it is my personal impression at the end of the session that anomaly detection is currently a bit overhyped and that the real challenges and problems to be resolved start after anomalies are detected.

Nevertheless, as Bryan summarizes: “ML is a very, very powerful tool.” 

However, strategically he seems not to see a lot of value in anomaly detection by itself, but rather: “Can we use machine learning (results) to change how we build networks in the future?”