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The role of artificial intelligence (AI) and machine learning (ML) in financial sector cybersecurity.

The banking industry has been an early adopter of AI (Artificial Intelligence) and machine learning (ML) employing the technology in many applications to enhance operations and customer experiences. Areas to improve customer service such as document verification and processing, voice and speech recognition, chatbots and virtual assistants, plus predictive analytics and personalization have been in use and gaining sophistication for some time.

AI has also been an established business tool in a security context to assist in the identification of irregular transactions indicating potential fraud. These types of high-volume, less sophisticated crimes lend themselves to the use of AI due to its strength in spotting patterns in large datasets. AI presents an effective means to tackle low-level crime quickly and efficiently for the scale that banks and payment businesses operate on. It also can play a key role in identifying suspicious (phishing) emails. However, it is the extended application of AI and ML in the fight against cybercrime which is gaining significant attention.

The application of AI and ML in anomaly detection.

AI and ML can continuously monitor network and system activities to detect unusual or suspicious behavior. When deviations from established baselines are identified, alerts can be generated for further investigation. The development team at Cristie Software has utilized ML technologies to offer an advanced system for detecting unusual file activity during the system recovery and replication phases. System backups are a critical defense against ransomware, and the backup procedure presents an excellent chance to compare the structure of files between successive backup images. While some files regularly change as part of normal business operations performed within their associated applications, the malicious encryption of files typically follows identifiable patterns. It is these patterns that Cristie Software’s anomaly detection technology aims to recognize, providing an early warning of a potential cyber attack in progress. Learn more in our earlier article covering our application of ML in advanced file anomaly detection.

AI and ML have a significant and evolving role in financial sector cybersecurity.

AI and Ml play a significant and evolving role in cybersecurity; anomaly detection is just one key area where the strength of these technologies come to the fore. Here are 10 additional key roles and applications of AI and ML in the field of financial sector cybersecurity:

  1. Threat Detection and Prevention: AI-powered systems can analyze vast amounts of data to identify patterns and anomalies indicative of potential cyber threats that might go unnoticed by traditional rule-based systems. AI and ML driven threat detection tools can recognize known malware and viruses and can also identify zero-day vulnerabilities by analyzing behavior patterns.
  2. User and Entity Behavior Analytics (UEBA): AI can analyze user and entity behavior to identify unauthorized or suspicious activities. It can spot unusual login patterns, access to sensitive data, or deviations from established usage patterns.
  3. Phishing Detection: AI can help in identifying phishing attempts by analyzing email content, sender behavior, and other indicators. It can reduce false positives and improve the accuracy of identifying phishing emails.
  4. Automated Incident Response: AI can automate incident response processes by providing real-time threat analysis and immediate actions to mitigate threats. This can help organizations respond to threats faster and reduce the impact of security incidents.
  5. Predictive Analysis: AI can use historical data to predict future security threats and vulnerabilities, enabling proactive measures to be taken to prevent attacks.
  6. Vulnerability Management: AI can assist in identifying and prioritizing vulnerabilities within a network or system, helping security teams focus their efforts on critical areas.
  7. Security Automation: AI can automate routine security tasks, freeing up security personnel to focus on more complex and strategic aspects of cybersecurity.
  8. Natural Language Processing (NLP): NLP-powered AI can help in analyzing and understanding unstructured data, such as security logs and reports, to extract actionable insights.
  9. Security Analytics: AI-driven security analytics platforms can provide a holistic view of an organization’s security posture by aggregating and analyzing data from various sources, facilitating better decision-making.
  10. Adaptive Security: AI can adapt security measures based on evolving threats and changing network conditions, providing a more dynamic and responsive defense mechanism.

Financial sector regulatory interest in AI and ML.

Regulators are also becoming increasingly interested in the risks and benefits presented by AI and ML technology. In 2020 the Bank of England (BoE) and the Financial Conduct Authority (FCA) launched the Artificial Intelligence Public-Private Forum (AIPPF) and issued their final report which represents the results of more than a year’s worth of meetings, workshops, and discussions focused on Data, Model Risk and Governance aspects of AI adoption. The BoE followed in October 2022 with the discussion paper DP5/22 – Artificial Intelligence and Machine Learning which aims to respond to the AI Public-Private Forum final report and gather further feedback on the regulators’ views of the risks and benefits of the use of AI, as well as how the current regulatory framework applies to AI and ML The UK FCA/PRA regulations governing Operational Resilience and the European Union (EU) Digital Operational Resilience Act (DORA) are both comprehensive operational resilience regulations that are seen as significant drivers of substantial investments in financial sector cybersecurity. They are regarded as the most extensive and impactful operational and cybersecurity regulations globally.

Learn how Cristie Software can complement Operational Resilience and Cybersecurity practices for the Financial Sector.

It is important to note that while AI has many benefits in financial sector cybersecurity, it’s not a panacea. It should be used in conjunction with other data security practices and technologies to create a comprehensive cybersecurity strategy. Moreover, AI systems themselves need to be protected from adversarial attacks to maintain their effectiveness in defending against cyber threats. Since its inception, Cristie Software has been dedicated to automating system recovery, replication, and migration using cutting-edge techniques and the latest computing advancements. Incorporating ransomware detection driven by Machine Learning (ML) is a seamless expansion of our disaster recovery capabilities and represents a unique strength of our system recovery tools. Our software suite can support all major cloud and virtualization platforms as targets for replication or recovery. Additionally, we offer specialized extensions to improve system recovery from leading backup solutions, including Dell Technologies Avamar, Dell Technologies Networker, IBM Spectrum Protect, Cohesity DataProtect, and Rubrik Security Cloud. Visit the CloneManager® and System Recovery product pages or contact the Cristie Software team for more information regarding the Cristie Software suite of solutions for system recovery, replication, migration, and ransomware protection.

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