How AI is Changing the Cyber Security Landscape in Finance

Finance providers’ proximity to multiple industries makes them an immensely important part of the digital landscape. Their interconnectedness – and the fact they are tasked with handling large amounts of capital – often make them an incredibly alluring target for cyberattackers.

This means that, knowingly or otherwise, finance is at the forefront of AI’s impact on cybersecurity. Whether it’s the security ramifications of AI-driven trading bots, or on-the-ground algorithms in security tools, it’s vital to know how AI is changing the cybersecurity landscape.

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How AI is Changing Enterprise Tools

Financial institutions are tasked with not just optimizing their current tech, but also paying increasing attention to the growing field of technologies that promise to:

Increase automation
Escalabilidade para demandas empresariais
Cost savings

AI is one of those technologies: while Machine Learning (ML) isn’t a new concept, the new applications of GenAI have proven themselves as here to stay.

One of the AI’s first dramatic impacts was the way in which enterprise applications are being developed. AI architecture is able to handle greater quantities of data than ever before – combined with the Software-as-a-Service (SaaS) model, organizations can scale faster than ever before.

This scale comes at a cost, though: for many cloud-based tools, data is exchanged by Application Programming Interfaces (APIs): if not managed, these can create a backlog of invisible connections. This is called shadow IT.

To mitigate this, many enterprises have decided not to use AI providers’ public API offerings – but instead create more in-house enterprise solutions, deployed on their own infrastructure. Even more costly is the use of Retrieval Augmented Generation (RAG) – this relies on a foundational model being fine-tuned off the company’s own proprietary data.

The result is AI-driven tools that are increasingly trained off the day-to-day activities of the organization.

In finance, this has driven a great deal of operational growth: like HR applications that allow authenticated employees to ask an LLM their unique query, this analyzes the organization’s HR policies and compares them against sensitive employee data. Because of the widespread use of organizations’ own sensitive data, leakage or AI manipulation techniques like jailbreaking now represent a considerable risk, drastically complicating the cybersecurity landscape.

How AI is Changing Financial Applications

AI is already a staple of several key financial practices. Some of the most common applications include:

Algorithmic Trading: AI-driven algorithms analyze market trends and historical data to execute trades with speed and precision, surpassing human capabilities.
Automation and Efficiency: By automating repetitive tasks, AI enables financial institutions to handle large datasets with improved accuracy and speed.
Regulatory Compliance: AI simplifies compliance by automating monitoring and reporting processes, helping meet regulatory standards and deadlines.
Credit Scoring: AI evaluates diverse data sources, such as social media and online behavior, to provide more precise assessments of creditworthiness.

Although AI has been integral to corporate and investment banking and insurance for decades, the rest of the financial sector is rapidly catching up, and the sector has been pushing for faster automation.

The ramifications that these have on cybersecurity can’t be ignored, however.

One risk is the way in which algorithms could unknowingly and individually push for collusion trading. AI traders could strategically manipulate low-order flows, even without explicit coordination. There are two primary reasons:

Homogenized learning biases
Collusion reinforced by punishment threats

Homogenized Learning Biases

Homogenized learning biases stem from “artificial stupidity.” This occurs when AI systems fail to sufficiently learn or adapt to information outside of their usual or expected datasets. Because many AI models share common foundational architectures, these learning biases are frequently copied and pasted across different AI systems, leading to homogenized behavior.

Such shared biases can unintentionally result in coordinated actions, as the algorithms arrive at similar pricing or strategic decisions without explicit communication. The result is a financial market that increasingly drifts away from real, fundamental pricing information.

Should a public pool of training data – or an entire foundational model – be compromised, market manipulation becomes one of many very real cyber threats.

Collusion Reinforced by Punishment Threats

The second mechanism involves collusion maintained through the implicit or explicit threat of punishment. Algorithms are trained to move toward agreed-upon behaviors through reward and punishment mechanisms.

This mirrors traditional economic concepts like price-trigger strategies (where any member of a cartel or cooperative group risks retaliation if they attempt to undercut the agreed-upon prices or strategies). In the context of algorithmic trading, an algorithm may rapidly adjust prices to eliminate competitive advantages gained by defectors – effectively eliminating independent trade decisions.

Together, these mechanisms highlight how algorithmic systems, even without explicit collusion, can align in ways that mimic coordinated anti-market activities. This all stems from the fact that ‘black box’ AI-powered systems are inherently obscure.

The Flipside: AI is Driving Security Tool Evolution

It’s not all bad news. AI’s ability to process vast amounts of data and uncover intricate patterns makes it particularly effective for huge swathes of cybersecurity maintenance. Many organizations heavily utilize automation to handle time-intensive and labor-heavy tasks related to fraud prevention and cybersecurity.

Artificial Intelligence and Generative AI can significantly enhance these efforts by processing larger, more complex datasets and applying advanced analytics.

For instance, Generative AI can be used to educate employees and customers about recognizing and mitigating potential threats and fraud risks. Additionally, it can analyze internal policies and communications to identify weaknesses, prioritize areas for improvement, and strengthen overall security measures.

Apply and Protect AI with Check Point Infinity

Keeping organizational assets safe has led to a lot of well-meaning hesitancy. In finance, however, every second counts: any delay risks organization agility and financial returns. To ensure robust protection, financial institutions need to adopt a multi-layered security strategy that safeguards networks, applications, and workloads. Check Point’s Infinity platform provides this.

Furthermore, Check Point’s GenAI protection allows enterprises to stay cutting-edge while applying suitable, AI-driven protection to every facet of the organization.

See how Check Point Infinity unlocks safe GenAI adoption with a demo, or explore how its Copilot function supports security admins, IT departments, and security operations teams in their day-to-day work, providing ground-level, real-time insight into the AI digital landscape.