Business

“Why cyber resilience is the banking sector’s top priority?”

Published

11 months ago

November 3, 2022

THE IMPORTANCE OF CYBERSECURITY AND HOW TO MEASURE IT

The financial sector has always been a top target for cyber criminals. But as adversaries grow more organised and sophisticated, how severe are the risks, and how can organisations stay ahead of bad actors?

We asked Raghu Nandakumara, Head of Industry Solutions at Illumio, how the financial sector can build resilience against the ever-growing threat of cyberattacks.

How vulnerable is the financial sector to cyber threats right now?

The critical nature of the financial sector makes it very vulnerable to widespread digital threats. Financial institutions collect colossal amounts of data, from personal information to credit card details, national insurance numbers, investment details, loan information and more. They are essentially trusted with our entire digital identities.

Now, alongside attacks seeking to steal personal data or access accounts, the sector is increasingly under siege from ransomware. Last year, the industry accounted for 6 per cent of the top ransomware attacks and attacks targeting financial organisations have nearly tripled in 2022.

Raghu Nandakumara

Attackers are also continuing to leverage double extortion approaches, combining data encryption to impact to operations with threats to leak or sell sensitive data. This creates more pressure on organisations to pay the ransom. According to reports, the financial sector has paid an average of $1.59M in ransom, higher than the global average of $1.4M.

Finally, institutes like banks are incredibly vulnerable to losses in productivity or operational downtime. Even the smallest disruption can have a huge impact on the business or the larger supply chain, since almost every individual and business are constantly reliant on banking services.

Why is cyber resilience so important for the financial sector?

Cyber resilience defines an organisation’s ability to detect, respond to, recover, and protect itself from cyberattacks. On a more granular level, developing cyber resilience means an organisation can remain operational, even in the event of an active attack. It’s not just the practice of stopping attackers from breaching your system, but rather fortifying them so they can still function, even when facing a breach.

Cyber resilience is critical for the financial sector because these organisations are the backbone of our economy and societies. If a banking organisation is unable to facilitate financial transactions, it will disrupt the wider marketplace for liquidity and assets. The disruption of financial services means a part of society’s capital flow will be completely halted.

Also, as we increasingly rely on digital and cashless transactions, disruption to such services can have crippling consequences. That’s why financial services rank as critical national infrastructure (CNI) alongside power and water.

How can financial firms strengthen their cyber resilience?

Achieving cyber resilience starts with visibility. Financial organisations often have a complex network infrastructure, comprised of many endpoints, interconnected systems and hybrid IT. This makes it hard for security teams to maintain visibility of their entire estate. But if you can’t see the risks, how can you defend against them?

To achieve visibility, organisations need to develop a clear picture of how their applications and workloads are communicating with each other. Then, they need to identify their most high-value assets and resources and define who has access to them. Not every employee within the company needs access to high-value assets. Limiting access to these resources to only a handful of individuals can prevent access privilege abuse and mitigate damage from a compromised account or endpoint.

Moreover, organisations must extend defences to every endpoint, cloud, or data centre resource connected to the network. This includes every mobile banking app, investment app, devices used by remote employees, and all third-party endpoints. Just protecting the core enterprise network alone is no longer enough.

Our research found that 74% of organisations expect Endpoint Detection and Response (EDR) to block or detect all malicious activity, yet most organisations still get breached. So, it’s clear that detection tools alone can no longer provide the protection needed against modern threats. Having visibility of all network traffic is critical. Effective monitoring frameworks must be in place to visualise endpoint traffic for every user or device that is accessing the network, supported by a Zero Trust strategy that always ‘assumes breach’.

Why is it essential to ‘assume breach’? And how can financial firms put the practice into action?

Assume breach is the approach of presuming that attacks and intrusions are inevitable and arranging the network’s defences to mitigate the impact. So, when a breach does occur, the network can automatically isolate adversaries before they traverse through different systems and inflict serious damage. With most attacks initiated and escalated through compromise or misuse of privileged accounts, an ‘assume breach’ mentality is critical, serving to shift defence strategies from a passive to an active framework.

Employing Zero Trust Segmentation (ZTS) is one of the most effective methods for making the assume breach model a reality. This Zero Trust technology is designed to divide an enterprise network, data centre, cloud environment or endpoint estate into multiple segments or subnets. Each segment has its own access and authentication policies, where user identities, devices, and network configurations must be validated every time a user requests access.

You can think of ZTS like a hotel. The hotel entrance is the perimeter and if someone gets into the hotel lobby (bypassing firewall defences) they don’t automatically have access to rooms. Guests have their own unique key cards with access to only the floors and room they need. So, if you are meant to check out at 11am and you try to access your room at 11:30 am, your access will be denied, and you will need to go to the front desk and get re-verified. ZTS functions in the same way, ensuring the division of endpoints, clouds and data centres into segments to protect them from potential threats.

It can also automatically block unauthorised movement across hybrid IT. For example, even if an attack compromises or reaches one device, the threat is contained to that single endpoint, preventing the spread of the breach across the organisation and limiting its impact. So, even if one part of the organisation falls victim to a breach, the bulk of business can continue as usual. That is true resilience and how financial firms can stay one step ahead of the attackers.

Financial organisations will continue to be targets of ransomware and other sophisticated threats. Cyber criminals follow the money and will always evolve their tactics to meet their aims. By putting in place proactive security measures, such as ZTS, coupled with an ‘assume breach’ mentality the financial sector can build resilience, and ensure critical systems remain operational and sensitive data protected, regardless of what threats come its way.

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In-platform solutions are only a short-term enhancement, but bespoke AI is the future

Published

13 hours ago

September 27, 2023

editorial

By Damien Bennett, Global Director, Principal Consultant, Incubeta

If you haven’t heard anyone talking about artificial intelligence (AI) yet, then where have you been? Conversations about AI and its advantages to society have been a key talking point over recent months, with advances being made in the generative AI race and ChatGPT opening a whole plethora of possibilities. Many have highlighted the advantages of AI, but notably it’s ability to create human-like content.

But these discussions have only scratched the surface of what AI is capable of doing. It is for far more than just essay writing, adding Eminem to your rave and photoshopping dogs into pictures.

In marketing, we have been using AI for years, for everything from analyzing customer behaviors to predicting market changes. It’s enabled us to segment customers, forecast sales and provide personalized recommendations, having a huge impact on how our industry works.

It is even, for the more savvy marketers of the world, becoming a key tool in maximizing budget efficiency – which is apt, considering over 70% of CMOs believe they lack sufficient budget to fully execute their 2023 strategy.

Now, as AI becomes more intelligent, the number of efficiencies it can unlock continues to rise. Not only can it help brands get the most out of their available resources and identify any areas of waste, but it can also help highlight new opportunities for growth and maximize the impact of your budget allocation.

The trick, however, is to veer away from the norm of using in-platform solutions with a one-size-fits-all approach and create your own, bespoke solutions that are tailored to your business needs.

Pitfalls of in-platform solutions

In-platform solutions aren’t by any means a bad thing. In fact, built-in AI tools have become increasingly popular, owing to their ease of integration, user-friendly interfaces and minimal set up requirements. They come pre-packaged with the platform, offering the user the ability to leverage AI technologies without the need for in-depth technical expertise or the upfront cost of building a solution from scratch.

However, the streamlined and accessible nature of in-platform AI solutions comes at the expense of complexity and customization. They are designed to serve a broad user base, but for the most part are built using narrow AI solutions with predefined features and workflows.

This makes them great for assisting with common AI tasks, but they lack the flexibility to tailor functionality towards unique business requirements or innovative use cases, limiting the potential efficiencies and cost savings that can be unlocked. Additionally, if a business’ competitors are using the same platform, they are probably using the same AI solution, meaning any strategic advantage gained from these will be reduced.

Bespoke AI solutions, on the other hand, may carry a higher initial investment – but can offer a significantly more attractive ROI over a short amount of time.

Why customized and adapted AI is the key

The difference between bespoke AI and in-platform solutions is similar to that between home cooked food and a microwave meal. Yes, it is more time consuming to prepare, and yes it likely carries more of an upfront cost, but the end result is going to be far more appealing and will carry more long-term value (financially… not nutritionally).

That’s because bespoke solutions, by nature, will have been tailored to address your brands specific needs and challenges. These custom-built tools allow for much greater efficiencies by streamlining workflows across different channels, automating more complex tasks, and providing deeper, more relevant insights.

The increased level of optimization can significantly improve productivity and reduce operational costs over time, offering a higher ROI. The increased flexibility of bespoke AI also allows brands to implement innovative use cases that can significantly differentiate them from their competitors.

The data analyzed can be specifically chosen to match business requirements, as can the outputs of the AI tool, providing a significant advantage when understanding and acting on the insights provided.

Additionally, these tools are, by nature, more scalable. They can be updated, upgraded and expanded as needs change, ensuring they continue delivering value as the business grows. They can also be designed to integrate with any existing IT infrastructure, from CRM systems and databases to marketing platforms and sales tools – leading to more efficient and effective decision-making.

Managing finances with AI

It’s no secret that AI in marketing automation has, and will continue to, revolutionize the way marketing is done. It has a bright, if slightly terrifying, future and can help CMOs to unlock new efficiencies, maximize the impact of their budgets and increase their ROI. And as this technology becomes more advanced, its impact will only increase.

But we already know that…and so does everyone else.

So, in order for businesses to make themselves stand out from the crowd , they must look to fully adopt the power of AI. Creating a customized and unique AI solution could be the way to set yourself apart from your competitors. A bespoke AI tool can provide brands and businesses with features unique to them and their business needs. As a result, companies will benefit from more useful data and better results to make more data-driven decisions for their business. Ultimately, this will help brands to maintain a competitive edge over their competitors, deliver ROI and most importantly optimize their budgets.

Business

Exploring the Transformative Potential and Ethical Challenges of AI in Wealth Management

Published

1 day ago

September 26, 2023

admin

Nuno Godinho, Group CEO of Industrial Thought Group

In recent years, the advent of AI has sparked both excitement and scrutiny within the Wealth Management industry. The technology’s capabilities, including but certainly not limited to generative AI algorithms like ChatGPT, offer a new dimension to data analysis, market prediction, and portfolio management. However, while it presents a promising avenue for enhancing decision-making and elevating client interaction, AI also carries inherent challenges that demand careful consideration.

Benefits of AI in Wealth Management:

In a world where CX is key, AI enables wealth managers to provide personalised advice, improved portfolio performance, real-time insights, and convenient access to information and support. Previously it has been impossible for advisors to deliver hyper-personalisation at scale; now, AI-driven customisation lets them tailor investment strategies and recommendations to their clients’ unique financial goals, risk tolerance, and investment horizon.

AI algorithms can also analyse vast amounts of data to identify trends and opportunities, resulting in potentially higher returns on investments. And, more widespread use of automation will gradually reduce the cost of wealth management services, meaning higher-quality investment advice at a lower price. This is critical as firms fight to stay relevant for modern investors disillusioned by traditional advisory firms and private banks.

Relationship-wise, there are many other advantages. AI-driven data analytics make it easier to gain a deeper understanding of an investor’s needs, preferences, and behaviours, all of which help to build long-term relationships. Through predictive analytics, firms can differentiate their service and proactively identify new investment opportunities, such as emerging market trends or underperforming assets. At the same time, chatbots and virtual assistants facilitate constant communication to answer queries and increase engagement. By strategically integrating AI technology into their operations, firms have the power to optimise top and bottom lines, strengthen client connections and position themselves for long-term growth.

Navigating the Ethical and Practical Challenges:

While AI holds remarkable potential, major obstacles must be overcome. With AI’s reliance on large amounts of data, ensuring client data confidentiality, managing consent, and complying with global data protection regulations like GDPR are significant challenges. Another issue is algorithmic bias – as AI learns from data, it may inadvertently perpetuate inequalities or biases present in the training datasets used. Vigilance is necessary to ensure that AI systems don’t amplify these issues. A key concern is the absence of standard governance, leading to a lack of accountability and transparency. Black-box algorithms can make decisions without providing clear explanations for their reasoning, making it difficult for clients and regulators to understand and trust AI-driven outcomes. Overall, the responsibility for AI-generated recommendations remains complex, requiring collaborative efforts to establish robust regulatory frameworks.

Striving for Data Integrity and Reliability:

The efficacy of AI-driven solutions hinges on the quality of training dataset they are supplied with and rely upon. Therefore, ensuring accurate, unbiased, and comprehensive datasets is paramount to generating trustworthy insights. The absence of standardised data sharing can lead to skewed results, ultimately impacting the quality of AI-generated advice. Transparency in data usage, validation, and generation reasoning will be pivotal to cultivating client trust and minimising systemic risks, which ties back to the absence of standard governance, as the output from AI-generated advice will only be as good as the data sets provided. We need to understand the “lineage” of all data used and generated by the algorithms. Until the industry can come to some accord on how we plan to use all of our respective data, it will be prone to various biases and fragmented advice, which will lead to liability and reliability issues down the line. It’s worthwhile wondering whether we can see the industry opening up in an age of data equals value.

The Role of Collaborative Partnerships:

Amidst these challenges, collaborative partnerships emerge as a potent avenue. Established wealth management firms can harness the expertise of FinTech AI companies to augment their capabilities while mitigating the risks associated with AI adoption. A symbiotic relationship, where innovative AI solutions are developed by trusted partners, helps safeguard against potential pitfalls and aligns with the pursuit of ethical, data-driven decision-making.

Looking Ahead: Striking a Balance for Sustainable Progress:

As we journey into the AI-powered future of wealth management, it’s evident that a balanced approach is essential. The integration of AI has the potential to expedite the transition to wealth management 4.0, revolutionising personalised client experiences and advisory services. However, this progress must be underpinned by clear ethical guidelines, data integrity, and collaborative partnerships. Striking this equilibrium promises not only a more informed, efficient, and personalised industry but also one that upholds the principles of transparency, accountability, and client trust.

In conclusion, AI’s impact on the wealth and asset management landscape is profound, offering unparalleled insights and opportunities. While navigating challenges will be crucial, a collective effort to harness AI’s power while ensuring its responsible application will pave the way for a resilient, future-forward industry.