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Why insurers must be on the lookout for ever-opportunistic cyber attackers

Source: Finance Derivative

By Paul Prudhomme, Head of Threat Intelligence Advisory at IntSights, a Rapid7 company

The insurance industry has long been a staple for cyber attacks. Criminals go where the money is, and the sector represents one of the most direct ways to access key personal and financial data that can be used to net an illicit profit.

More recently, insurers have faced even greater risk exposure due to their provision of cyber insurance coverage, particularly when it comes to ransomware. The sector has also seen increased attention from state-sponsored actors seeking personal data to fuel other campaigns.

Why is the insurance sector such a popular target for cyber crime?

Threat actors regard the insurance industry as a valuable source of personally identifiable information (PII) which can be used for a variety of crimes, including identity theft, other types of fraud, and further cyber attacks.

Alongside insurance documentation itself, firms will also have digital copies of items such as passports, driver’s licenses and bank statements that have been used to verify the policy holder’s identity and address. Birth dates are also particularly valuable to criminals, alongside National Insurance numbers, Social Security numbers, and their various international equivalents.

In one prominent example, U.S. insurer Ryan Specialty Group had its employee email accounts breached in April 2021. Customer names, Social Security numbers, driver’s license and passport details, and financial account details were believed to be exposed as a result.

The depth of information held by insurers on behalf of policyholders is also useful to state-sponsored threat actors, providing a large amount of data for human intelligence (HUMINT) operations or signals intelligence (SIGINT) operations.

Insurers that provide cyber insurance also face an elevated threat level. Attackers may seek to compromise their network to unearth policy details and security standards as a way of creating more effective targeted attacks.

The rising threat of ransomware

In addition to data theft, insurers are also targets for ransomware attacks. Ransomware has swiftly risen to become one of the primary cyber threats for businesses in all industries today as an infection can rapidly cripple the organisation by encrypting key files and systems. Criminals are also increasingly coupling ransom demands with data theft, often threatening to leak sensitive information unless additional payment demands are met.

However, insurers that provide cyber policies may again face increased risk from organised cyber criminal gangs and state-backed actors. In one prominent example, the Asian component of global cyber insurer AXA was struck by the Avaddon ransomware last year very shortly after announcing that it would stop reimbursing new French customers that chose to pay ransom demands.

The group responsible may have been seeking to make an example of AXA, as its previous policy of covering ransom payments would make it more likely for victims to pay up to criminals.

Why most stolen data is destined for the dark web

Stolen data is a commodity item in the shadow economy maintained by cyber criminals. Datasets are readily bought and sold on hidden forums and marketplaces on the dark web, with individuals and groups often specialising in selling data rather than using it themselves.

In one example discovered by IntSights security researchers, a Chinese-speaking criminal going by “Rebecca” was selling access to records from Chinese auto insurance companies for $3 each. These records included PII such as names, addresses, and driver’s license numbers.

Threat actors will commonly purchase PII sets from different sources to help facilitate further data theft and fraud. The insurance sector is a favourite target here as automated quote tools can potentially be exploited into revealing more information about customers. Farmers Insurance Group, for example, revealed that in early 2021, attackers attempted to use previously stolen customer names, dates of birth, and street addresses to trick its automated car insurance tool into providing driver’s license numbers.

Criminal groups now often include the threat of data disclosure as part of ransomware attacks. Defiant organisations that refuse to pay up will be punished by having their data sold on the dark web, or sometimes dumped on publicly available open web platforms. The threat aims to pile additional pressure on the victim by creating a high-profile breach that will damage customer trust and attract the attention of compliance regulators.

How can insurance firms protect themselves and their customers?

All firms operating in the insurance sector should be aware that they represent a high priority target to threat actors ranging from opportunistic criminals to highly organised gangs and even state-sponsored groups. Securing the customer data in their care should be a top priority for all insurance firms.

Insurers need to consider the context of their data and how best to protect it. B2C security measures will be significantly different from B2B equivalents, for example, and different subsectors such as auto and health insurance will also have their own security threats and priorities.

Threat intelligence is the most important asset for attempting to understand and mitigate these risks. Having access to a range of data from open and closed web sources will help insurers to build a picture of threats arrayed against them and prioritise their security strategies accordingly.

This includes insight into general trends, such as new attack tactics, malware variants, and software vulnerabilities, and can also reveal direct threats to the organisation. For example, threat intelligence might uncover discussions in a dark web forum about targeting a specific insurer because of their ransomware pay-out policy, or due to an exploit in their automated customer service system.

Effective threat intelligence can also alert insurers to the fact they have been breached by discovering criminals arranging the sale of stolen data. While the firm will still suffer reputational and financial damage, this warning can give them a chance to get ahead of the crisis.

The cyber threat landscape has become increasingly hostile for the insurance sector in recent years. In order to have the best chance of protecting both themselves and their customers, insurance providers should look to implement threat intelligence to understand the context of their data and mitigate threats accordingly.

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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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