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The cloud: an immense asset companies should protect

Source: Finance Derivative

Chas Moloney, Marketing Director, Ricoh UK & Ireland  

With the advent of remote and hybrid working solutions, cloud systems have fast become the norm for the majority – if not all – businesses. The driving need to set up new working structures which allow for greater team agility forced companies to identify ways to transport the office into the employee’s home. The cloud was a perfectly natural fit.

Cloud software offers an incredible level of flexibility, independence and accessibility from everywhere, requiring nothing more than a stable internet connection. For us at Ricoh, the cloud has always represented a vast possibility of growth. We are well accustomed to working in the cloud and, as an integral part of how we do business, this isn’t going to change. We will continue to evolve our cloud technology and study its evolution and crucial role in workplace development. Even in a physical context such as the office, the cloud is an incredible asset to increase productivity and facilitate exchanging and reviewing documents and data.

However, the future of the cloud can’t lie in simply purchasing more memory space – it’s much more than that. Companies need to step forward and develop a distributed system that can allow better management and mitigate any department silos in the business. Cloud, at its most effective, is a collaboration tool. Meaning that the full potentiality of the cloud can only be properly realised if this system is used in the right way by leaders, IT professionals and employees. In fact, in our report Leading Change at Work, we found that across all businesses – public and private –  siloed decision making and working practices were the biggest hindrances to productive working.

Therefore, good management of the cloud relies on how it’s managed, distributed, and secured. Distributed cloud environments offer different cloud options, which offers a personalised service so companies can tailor both employee and customer services and outputs. The ability to leverage and scale data across borders by utilising distributed cloud offers businesses an unparalleled opportunity to provide a new form of professional services. Essentially, the cloud can be an incredibly effective tool when it is personalised to the need of the company and the customers.

This isn’t to say the cloud doesn’t have its flaws – like all facets of remote working, the biggest impact on uptime and resilience is a poor network and support from outdated legacy tools. Physical offices are installed with the best high-speed connectivity from the start, but people’s homes are a different matter. A slow internet connection can be frustrating, and it can diminish the tasks completed and cause issues with things like conference calls which will be imperative during this time. Cloud services that are slowed down due to reduced connectivity speeds are not just a nuisance but a cost to a business – it’s effectively a waste of paid licence fees. To prevent this, businesses should create and implement a cloud-based technology platform to create a network environment that can facilitate mobility and increase efficiency. They should also ensure employees have the right hardware to sit alongside their more modern software.

Outdated hardware also poses an incredible threat to cybersecurity, with data security only as strong as your weakest router. The rapid digital transformation towards remote and mobile workstations has exposed private data both of employees and companies to higher risks, making increasingly urgent the need to safeguard personal data and enhance cybersecurity activities.

Today, awareness around cybersecurity and data protection is higher than ever in the past. According to our 2020 report, The Conscious Workplace, 1/5 of managers were worried about the security of their network connections. Investing in these networks will undoubtedly make life easier for your employees, but it will also help prevent security threats. Unfortunately, connecting devices and entire workspaces to the internet can leave you exposed to cyber-attacks, so making sure your network is the best it can be is more critical than ever.

Ultimately, the cloud is a great resource that should be exploited and protected. With hybrid working likely to stick around for longer than anyone originally planned, careful examination and investment must be paid to ensure greater longevity and safety.

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Business

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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Technology

‘Aligning AI expectations with AI reality’

By Nishant Kumar Behl, Director of Emerging Technologies at OneAdvanced

AI is transforming the way we work now and will continue to make great strides into the future. In many of its forms, it demonstrates exceptional accuracy and a high rate of correct responses. Some people worry that AI is too powerful, with the potential to cause havoc on our socio-political and economic systems. There is a converse narrative, too, that highlights some of the surprising and often comical mistakes that AI can produce, perhaps with the intention of undermining people’s faith in this emerging technology.

This tendency to scrutinise the occasional AI mishap despite its frequent correct responses overshadows the technology’s overall reliability, creating an unfairly high expectation for perfection. With a singular focus on failure, it is, therefore, no surprise that almost 80% of AI projects fail within a year. Considering all of the hype around AI and particularly GenAI over the past few years, it is understandable that users feel short-changed when their extravagant expectations are not met.

We shouldn’t forget that a lot of the most useful software we all rely on in our daily working lives contains bugs. They are an inevitable and completely normal byproduct of developing and writing code. Take a look at the internet, awash with comments, forums, and advice pages to help users deal with bugs in commonly used Apple and Microsoft word processing and spreadsheet apps.

If we can accept blips in our workhorse applications, why are we holding AI to such a high standard? Fear plays a part here. Some may fear AI can do our jobs to a much higher standard than we can, sidelining us. No technology is smarter than humans. As technology gets smarter, it pushes humans to become smarter. When we collaborate with AI, the inputs of humans and artificial intelligence work together, and that’s when magic happens.

AI frees up more human time and lets us be creative, focusing on more fulfilling tasks while the technology does the heavy lifting. But AI is built by humans and will continue to need people asking the right questions and making connections based on our unique human sensibility and perception if it is to become more accurate, useful, and better serve our purpose.

The fear of failing to master AI implementation might be quite overwhelming for organisations. In some cases, people are correct in being cautious. There is a tendency now to expect all technology solutions to have integrated AI functionality for the sake of it, which is misguided. Before deciding on any technology, users must first identify and understand the problem they are trying to solve and establish whether AI is indeed the best solution. Don’t be blinded by science and adopt the whistles and bells that aren’t going to deliver the best results.

Uncertainty and doubt will continue to revolve around the subject of AI, but people should be reassured that there are many reliable, ethical technology providers developing safe, responsible, compliant AI-powered products. These organisations recognise their responsibility to develop products that offer long-term value rather than generating temporary buzz. By directly engaging with customers to understand their needs and problems, a customer-focused approach helps identify whether AI can effectively address the issues at hand before proceeding down the AI route.

In any organisation, the leader’s job is to develop strategy, ask the right questions, provide direction, and often devise action plans. When it comes to AI, we will all need to adopt that leadership mindset in the future, ensuring we are developing the right strategy, asking insightful questions, and devising an effective action plan that enables the engineers to execute appropriate AI solutions for our needs.

Organisations should not be afraid to experiment with AI solutions and tools, remembering that in every successful innovation, there will be some failure and frustration. The light bulb moments rarely happen overnight, and we must all adjust our expectations so that AI can offer a perfect solution. There will be bugs and problems, but the journey towards improvement will result in achieving long-term and sustainable value from AI, where everyone can benefit.

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Nishant Kumar Behl is Director of Emerging Technologies at OneAdvanced, a leading provider of sector-focussed SaaS software, headquartered in the UK.

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Business

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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