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The generative AI revolution is here – but is your cloud network ready to embrace it?

Paul Gampe, Chief Technology Officer, Console Connect

Generative Artificial Intelligence is inserting itself into nearly every sector of the global economy as well as many aspects of our lives. People are already using this groundbreaking technology to query their bank bills, request medical prescriptions, and even write poems and university essays.

In the process, generative AI has the potential to unlock trillions of dollars in value for businesses and radically transform the way we work. In fact, current predictions suggest generative AI could automate up to 70 percent of employees’ time today.

Paul Gampe

But regardless of the application or industry, the impact of generative AI can be most keenly felt in the cloud computing ecosystem.

As companies rush to leverage this technology in their cloud operations, it is essential to first understand the network connectivity requirements – and the risks – before deploying generative AI models safely, securely, and responsibly.

Data processing

One of the primary connectivity requirements for training generative AI models in public cloud environments is affordable access the scale of datasets. By their very definition, large language models (LLM) are extremely large. To train these LLMs requires vast amounts of data and hyper-fast compute      and the larger the dataset the more the demand for computing power.

The enormous processing power required to train these LLMs is only one part of the jigsaw. You also need to manage the sovereignty, security, and privacy requirements of the data transiting in your public cloud. Given that 39 percent of businesses experienced a data breach in their cloud environment in 2022, it makes sense to explore the private connectivity products on the market which have been designed specifically for high performance and AI workloads.

Regulatory trends

Companies should pay close attention to the key public policies and regulation trends which are rapidly emerging around the AI landscape. Think of a large multinational bank in New York that has 50 mainframes on its premises where they keep their primary computing capacity; they want to do AI analysis on that data, but they cannot use the public internet to connect to these cloud environments because many of their workloads have regulatory constraints. Instead, private connectivity affords them the ability to get to where the generative AI capability exists and sits within the regulatory frameworks of their financing industry.

Even so, the maze of regulatory frameworks globally is very complex and subject to change. The developing mandates of the General Data Protection Regulation (GDPR) in Europe, as well as new GDPR-inspired data privacy laws in the United States, have taken a privacy-by-design approach whereby companies must implement techniques such as data mapping and data loss prevention to make sure they know where all personal data is at all times and protect it accordingly.

Sovereign borders

As the world becomes more digitally interconnected, the widespread adoption of generative AI technology will likely create long-lasting challenges around data sovereignty. This has already prompted nations to define and regulate their own legislation regarding where data can be stored, and where the LLMs processing that data can be housed.

Some national laws require certain data to remain within the country’s borders, but this does not necessarily make it more secure. For instance, if your company uses the public internet to transfer customer data to and from London on a public cloud service, even though it may be travelling within London, somebody can still intercept that data and route it elsewhere around the world.

As AI legislation continues to expand, the only way your company will have assurance of maintaining your sovereign border may be to use a form of private connectivity while the data is in transit. The same applies to AI training models on the public cloud; companies will need some type of connectivity from their private cloud to their public cloud where they do their AI training models, and then use that private connectivity to bring their inference models back.

Latency and network congestion
Latency is a critical factor in terms of interactions with people. We have all become latency sensitive, especially with the volume of voice and video calls that we experience daily, but the massive datasets used for training AI models can lead serious latency issues on the public cloud.

For instance, if you’re chatting with an AI bot that’s providing you customer service and latency begins to exceed 10 seconds, the dropout rate accelerates. Therefore, using the public internet to connect your customer-facing infrastructure with your inference models is potentially hazardous for a seamless online experience, and a change in response time could impact your ability to provide meaningful results.

Network congestion, meanwhile, could impact your ability to build models on time. If you have significant congestion in getting your fresh data into your LLMs it’s going to start to backlog, and you won’t be able to achieve the learning outcomes that you’re hoping for. The way to overcome this is by having large pipes to ensure that you don’t encounter congestion in moving your primary data sets into where you’re training your language model.

Responsible governance

One thing everybody is talking about right now is governance. In other words, who gets access to the data and where is the traceability of the approval of that data available?

Without proper AI governance, there could be high consequences for companies that may result in commercial and reputational damage. A lack of supervision when implementing generative AI models on the cloud could easily lead to errors and violations, not to mention the potential exposure of customer data and other proprietary information. Simply put, the trustworthiness of generative AI all depends on how companies use it.

Examine your cloud architecture

Generative AI is a transformative field with untold opportunities for countless businesses, but IT leaders cannot afford to get their network connectivity wrong before deploying its applications.

Remember, data accessibility is everything when it comes to generative AI, so it is essential to define your business needs in relation to your existing cloud architecture. Rather than navigating the risks of the public cloud, the high-performance flexibility of a Network-as-a-Service (NaaS) platform can provide forward-thinking companies with a first-mover advantage.

The agility of NaaS connectivity makes it simpler and safer to adopt AI systems by interconnecting your clouds with a global network infrastructure that delivers fully automated switching and routing on demand. What’s more, a NaaS solution also incorporates the emerging network technology that supports the governance requirements of generative AI for both your broader business and the safeguarding of your customers.

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Driving business success in today’s data-driven world through data governance

Source: Finance derivative

Andrew Abraham, Global Managing Director, Data Quality, Experian

It’s a well-known fact that we are living through a period of digital transformation, where new technology is revolutionising how we live, learn, and work. However, what this has also led to is a significant increase in data. This data holds immense value, yet many businesses across all sectors struggle to manage it effectively. They often face challenges such as fragmented data silos or lack the expertise and resources to leverage their datasets to the fullest.

As a result, data governance has become an essential topic for executives and industry leaders. In a data-driven world, its importance cannot be overstated. Combine that with governments and regulatory bodies rightly stepping up oversight of the digital world to protect citizens’ private and personal data. This has resulted in businesses also having to comply e with several statutes more accurately and frequently.

We recently conducted some research to gauge businesses’ attitudes toward data governance in today’s economy. The findings are not surprising: 83% of those surveyed acknowledged that data governance should no longer be an afterthought and could give them a strategic advantage. This is especially true for gaining a competitive edge, improving service delivery, and ensuring robust compliance and security measures.

However, the research also showed that businesses face inherent obstacles, including difficulties in integration and scalability and poor data quality, when it comes to managing data effectively and responsibly throughout its lifecycle.

So, what are the three fundamental steps to ensure effective data governance?

Regularly reviewing Data Governance approaches and policies

Understanding your whole data estate, having clarity about who owns the data, and implementing rules to govern its use means being able to assess whether you can operate efficiently and identify where to drive operational improvements. To do that effectively, you need the right data governance framework. Implementing a robust data governance framework will allow businesses to ensure their data is fit for purpose, improves accuracy, and mitigates the detrimental impact of data silos.

The research also found that data governance approaches are typically reviewed annually (46%), with another 47% reviewing it more frequently. Whilst the specific timeframe differs for each business, they should review policies more frequently than annually. Interestingly, 6% of companies surveyed in our research have it under continual review.

Assembling the right team

A strong team is crucial for effective cross-departmental data governance.  

The research identified that almost three-quarters of organisations, particularly in the healthcare industry, are managing data governance in-house. Nearly half of the businesses surveyed had already established dedicated data governance teams to oversee daily operations and mitigate potential security risks.

This strategic investment highlights the proactive approach to enhancing data practices to achieve a competitive edge and improve their financial performance. The emphasis on organisational focus highlights the pivotal role of dedicated teams in upholding data integrity and compliance standards.

Choose data governance investments wisely

With AI changing how businesses are run and being seen as a critical differentiator, nearly three-quarters of our research said data governance is the cornerstone to better AI. Why? Effective data governance is essential for optimising AI capabilities, improving data quality, automated access control, metadata management, data security, and integration.

In addition, almost every business surveyed said it will invest in its data governance approaches in the next two years. This includes investing in high-quality technologies and tools and improving data literacy and skills internally.  

Regarding automation, the research showed that under half currently use automated tools or technologies for data governance; 48% are exploring options, and 15% said they have no plans.

This shows us a clear appetite for data governance investment, particularly in automated tools and new technologies. These investments also reflect a proactive stance in adapting to technological changes and ensuring robust data management practices that support innovation and sustainable growth.

Looking ahead

Ultimately, the research showed that 86% of businesses recognised the growing importance of data governance over the next five years. This indicates that effective data governance will only increase its importance in navigating digital transformation and regulatory demands.

This means businesses must address challenges like integrating governance into operations, improving data quality, ensuring scalability, and keeping pace with evolving technology to mitigate risks such as compliance failures, security breaches, and data integrity issues.

Embracing automation will also streamline data governance processes, allowing organisations to enhance compliance, strengthen security measures, and boost operational efficiency. By investing strategically in these areas, businesses can gain a competitive advantage, thrive in a data-driven landscape, and effectively manage emerging risks.

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The Benefits of EV Salary Sacrifice: A Guide for Employers and Employees

As the UK government continues to push for greener initiatives, electric cars have become increasingly popular. The main attraction for both employers and employees is the EV salary sacrifice scheme.

By participating in an EV salary sacrifice scheme, both employers and employees can enjoy cost savings and contribute to environmental sustainability along the way! This article will delve into the specifics of how these schemes operate, the financial advantages they offer, and the broader positive impacts on sustainability.

We will provide a comprehensive overview of the mechanics behind EV salary sacrifice schemes and discuss the various ways in which they benefit both employees and employers, ultimately supporting the transition to a greener future in the UK.

What is an EV Salary Sacrifice Scheme?

An EV salary sacrifice scheme is a flexible financial arrangement that permits employees to lease an EV through their employer. The key feature of this scheme is that the leasing cost is deducted directly from the employee’s gross salary before tax and National Insurance contributions are applied. By reducing the taxable income, employees can benefit from substantial savings on both tax and National Insurance payments. This arrangement not only makes EVs more affordable for employees but also aligns with governmental incentives to reduce carbon emissions.

For employers, implementing an EV salary sacrifice scheme can lead to cost efficiencies as well. The reduction in National Insurance contributions on the employee’s reduced gross salary can offset some of the costs associated with administering the scheme. Additionally, such programmes can enhance the overall benefits package offered by the employer, making the company more attractive to prospective and current employees.

Benefits for Employees

1. Tax and National Insurance Savings

By opting for an EV salary sacrifice scheme, employees can benefit from reduced tax and National Insurance contributions. Since the lease payments are made from the gross salary, the taxable income decreases, resulting in substantial savings.

2. Access to Premium EVs

Leading salary sacrifice car schemes often provide access to high-end electric vehicles that might be otherwise unaffordable. Employees can enjoy the latest EV models with advanced features, contributing to a more enjoyable and environmentally friendly driving experience.

3. Lower Running Costs

Electric vehicles typically have lower running costs compared to traditional petrol or diesel cars. With savings on fuel, reduced maintenance costs, and exemptions from certain charges (such as London’s Congestion Charge), employees can enjoy significant long-term financial benefits.

4. Environmental Impact

Driving an electric vehicle reduces the carbon footprint and supports the UK’s goal of achieving net-zero emissions by 2050. Employees can take pride in contributing to a cleaner environment.

Benefits for Employers

1. Attract and Retain Talent

Offering an EV salary sacrifice scheme can enhance an employer’s benefits package, making it more attractive to potential recruits. It also helps in retaining current employees by providing them with valuable and cost-effective benefits.

2. Cost Neutrality

For employers, EV salary sacrifice schemes are often cost-neutral. The savings on National Insurance contributions can offset the administrative costs of running the scheme, making it an economically viable option.

3. Corporate Social Responsibility (CSR)

Implementing an EV salary sacrifice scheme demonstrates a commitment to sustainability and corporate social responsibility. This can improve the company’s public image and align with broader environmental goals.

4. Employee Well-being

Providing employees with a cost-effective means to drive electric vehicles can contribute to their overall well-being. With lower running costs and the convenience of driving a new EV, employees may experience reduced financial stress and increased job satisfaction.

How to Implement an EV Salary Sacrifice Scheme

1. Assess Feasibility

Evaluate whether an EV salary sacrifice scheme is feasible for your organisation. Consider the number of interested employees, potential cost savings, and administrative requirements.

2. Choose a Provider

Select a reputable provider that offers a range of electric vehicles and comprehensive support services. Ensure they can handle the administrative tasks and provide a seamless experience for both the employer and employees.

3. Communicate the Benefits

Educate your employees about the advantages of the scheme. Highlight the financial savings, environmental impact, and access to premium EV models. Provide clear guidance on how they can participate in the programme.

4. Monitor and Review

Regularly review the scheme’s performance to ensure it continues to meet the needs of your employees and the organisation. Gather feedback and make adjustments as necessary to enhance the programme’s effectiveness.

Conclusion

The EV salary sacrifice scheme offers a win-win situation for both employers and employees in the UK. With significant financial savings, access to premium vehicles, and a positive environmental impact, it’s an attractive option for forward-thinking organisations. By implementing such a scheme, employers can demonstrate their commitment to sustainability and employee well-being, while employees can enjoy the benefits of driving an electric vehicle at a reduced cost.

Adopting an EV salary sacrifice scheme is a step towards a greener, more sustainable future for everyone.

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Machine Learning Interpretability for Enhanced Cyber-Threat Attribution

Source: Finance Derivative

By: Dr. Farshad Badie,  Dean of the Faculty of Computer Science and Informatics, Berlin School of Business and Innovation

This editorial explores the crucial role of machine learning (ML) in cyber-threat attribution (CTA) and emphasises the importance of interpretable models for effective attribution.

The Challenge of Cyber-Threat Attribution

Identifying the source of cyberattacks is a complex task due to the tactics employed by threat actors, including:

  • Routing attacks through proxies: Attackers hide their identities by using intermediary servers.
  • Planting false flags: Misleading information is used to divert investigators towards the wrong culprit.
  • Adapting tactics: Threat actors constantly modify their methods to evade detection.

These challenges necessitate accurate and actionable attribution for:

  • Enhanced cybersecurity defences: Understanding attacker strategies enables proactive defence mechanisms.
  • Effective incident response: Swift attribution facilitates containment, damage minimisation, and speedy recovery.
  • Establishing accountability: Identifying attackers deters malicious activities and upholds international norms.

Machine Learning to the Rescue

Traditional machine learning models have laid the foundation, but the evolving cyber threat landscape demands more sophisticated approaches. Deep learning and artificial neural networks hold promise for uncovering hidden patterns and anomalies. However, a key consideration is interpretability.

The Power of Interpretability

Effective attribution requires models that not only deliver precise results but also make them understandable to cybersecurity experts. Interpretability ensures:

  • Transparency: Attribution decisions are not shrouded in complexity but are clear and actionable.
  • Actionable intelligence: Experts can not only detect threats but also understand the “why” behind them.
  • Improved defences: Insights gained from interpretable models inform future defence strategies.

Finding the Right Balance

The ideal model balances accuracy and interpretability. A highly accurate but opaque model hinders understanding, while a readily interpretable but less accurate model provides limited value. Selecting the appropriate model depends on the specific needs of each attribution case.

Interpretability Techniques

Several techniques enhance the interpretability of ML models for cyber-threat attribution:

  • Feature Importance Analysis: Identifies the input data aspects most influential in the model’s decisions, allowing experts to prioritise investigations.
  • Local Interpretability: Explains the model’s predictions for individual instances, revealing why a specific attribution was made.
  • Rule-based Models: Provide clear guidelines for determining the source of cyber threats, promoting transparency and easy understanding.

Challenges and the Path Forward

The lack of transparency in complex ML models hinders their practical application. Explainable AI, a field dedicated to making models more transparent, holds the key to fostering trust and collaboration between human and machine learning. Researchers are continuously refining interpretability techniques, with the ultimate goal being a balance between model power and decision-making transparency.

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