Network automation and artificial intelligence (AI) have emerged as buzzwords in the global telecom domain. Telecom operators across the globe are warming up to the idea of upgrading to a network that is programmable, agile and flexible enough to facilitate innovative use cases. The global AI market was valued at $136.55 billion in 2022 and is expected to reach a compound annual growth rate (CAGR) of 37.3 per cent between 2023 and 2030. Similarly, the global network automation market is expected to experience a CAGR of 23.14 per cent from 2023 to 2032 and is expected to be valued at $28.63 billion.
Owing to the rapid deployment of network automation and AI security tools, certain risks have emerged that cannot be overlooked. While network automation tools and AI allow enterprises to enhance network security more effectively and efficiently to meet business demands, they also give rise to a significant number of concerns regarding their utilisation.
A look at the key challenges faced while deploying AI and automating networks…
Network automation challenges
Integration
The integration of different network automation tools with existing network infrastructure can be a complex and time-consuming task. This is because different tools often use different application programming interfaces (APIs) and protocols, and they may not be compatible with each other. As a result, ensuring the seamless operation of all tools becomes challenging, potentially leading to errors, inefficiencies and security risks.
Moreover, organisations typically have a heterogeneous network infrastructure, which means that they use various network devices from different vendors. This makes it difficult to integrate different automation tools, as each tool may only be compatible with a specific set of devices.
There is no universal standard for network automation, which means that each tool may have its own way of operating, compounding the challenge of integrating them, as they might not be able to communicate with each other or share data.
Network automation can be a complex task, even when using a single tool. Integrating multiple tools can add to the complexity, making it more difficult to manage and troubleshoot the entire system.
Complex toolsets
Network automation requires a thorough comprehension of network protocols, operating systems and software tools. This can be challenging for network administrators who may not have the necessary expertise or experience.
Although automation tools enhance task efficiency and effectiveness, they also introduce new complexities. This is especially true when AI and machine learning (ML) are added to the mix. AI and ML make automation tools more powerful and sophisticated, but they also make them more difficult to understand and control. Therefore, it is important to carefully consider the risks and benefits of employing automation tools, especially when AI and ML are involved.
Distributed networks
Distributed networks, such as hybrid and multi-cloud environments, are often more efficient and scalable than traditional single-cloud environments. However, they can also be more complex and difficult to manage. If automation tools are not compatible with these distributed networks, it can hinder efficiency and scalability.
Incompatible automation tools can pose new challenges, such as the inability to establish communication and manage workloads across various clouds. This can lead to errors, inefficiencies and scalability problems.
Therefore, it is crucial to select automation tools that are compatible with distributed networks. These tools should be able to automate tasks across different clouds, manage workloads efficiently and ensure seamless network operations.
Higher costs
The implementation of network automation can be a costly endeavour, particularly for small and medium-sized enterprises. This is due to the need for software licenses, hardware and training. Additionally, the complexity and time-consuming nature of the implementation process can further contribute to the overall expense. As a result, some organisations may choose not to implement network automation, despite the fact that it could provide several advantages to their operational efficiency.
Maintenance
Automated scripts and templates are indispensable tools for network automation. However, their maintenance is essential to ensure that they continue to function correctly. This can be a time-consuming task, as it necessitates frequent updates to keep up with changes in the network environment. Changes to network devices, operating systems and software can impact the performance of automated scripts and templates. Hence, it is critical to have a process in place for regularly testing and updating these scripts and templates.
Network device compatibility
The lack of open APIs in legacy network devices can hinder the effectiveness of network automation. This is because these devices are often locked into proprietary ecosystems that cannot be controlled by third-party tools. As a result, the range of automation tools available for managing these devices is limited, which complicates the automation of tasks that require multiple devices in order to work together.
By troubleshooting these challenges, a company can mitigate the risks associated with employing different network automation tools and ensure that the network is managed efficiently and securely.
Challenges associated with AI deployment
The majority of challenges encountered in network automation are related to the deployment of AI. These include:
Unethical use of AI
Despite significant investments in security tools and platforms, organisations continue to face security challenges. One of the major reasons behind this is the adoption of AI by cybercriminals for malicious attacks, to make them more devastating. This further limits the ability of an organisation to utilise AI defensively.
AI system bias
Bias can creep into algorithms in a variety of ways. Since AI systems learn to make decisions based on training data, they may inadvertently adopt biases from the information provided. As stated in the Artificial Intelligence Index Report 2023, published by Stanford University, an AI system becomes biased when its output reinforces and perpetuates stereotypes that harm specific groups. There have been numerous instances of such biases in the judicial system and even in hiring practices within various businesses, all of which have involved AI. As a result, the data supplied to AI platforms must be carefully regulated and examined beforehand.
Malware signatures
Malware signatures are unique patterns of code that can be used to identify malicious software. Security teams employ these signatures to scan for malware and trigger alerts upon detection. However, the growth of malware signatures is not keeping pace with the surge in new malware threats. This is because malware authors can easily change the code of their malware to evade detection by signatures.
To tackle these challenges, AI systems can be incorporated for malware detection. AI systems are trained on large amounts of data, including malware samples, to learn how to identify malware. However, this data can be difficult and expensive to obtain, and it often becomes obsolete by the time it is utilised to train AI.
Adoption barriers
Managing AI security systems is a costly endeavour, similar to the costs associated with deploying network automation tools. As a result, very few businesses have integrated AI security systems into their cybersecurity application services. Some of the major cost-related challenges that prevent the widespread deployment of AI solutions include:
- Hardware costs: AI security systems require specialised hardware, such as high-performance servers and graphic processing units.
- Software costs: It is essential to deploy specific software in AI security for better data collection and analysis. These software solutions encompass ML algorithms and data analytics tools, which have expensive software licenses.
- Training costs: AI security systems require training data, which can be expensive to collect and label.
- Maintenance costs: AI security systems maintenance can entail significant costs. A routine maintenance practice for AI security systems would include hardware, software and data maintenance. In addition to these routine maintenance tasks, AI security systems might require more specialised maintenance activities, such as tuning system parameters, investigating and responding to incidents and conducting system testing.
Future outlook
At present, both network automation and AI tools inherently lack the features and capabilities to fully protect networks. Going forward, securing these networks would require the adoption of a holistic multi-layered approach. It is imperative that challenges associated with these technologies are ironed out as they continue to reach a wider array of use cases.
