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Financial Services

Data Aggregation and Artificial Intelligence in Financial Services

My last blog highlighted security audits and penetration testing. This final blog of the series discusses security data aggregation and artificial intelligence (AI) implementations in financial services.

Should a breach occur, gathering log data from all critical security, network, and endpoint devices is key to a forensic investigation to determine how the firm’s defenses were violated and what data has been compromised. The content of the log files from each type of device (firewalls, routers, LDAP, web, database, and application servers) will differ, and each will likely hold clues to determining the attack vector used. It should be noted that similar devices from different vendors will have different formats and may log different information depending on their configuration, which will complicate the analysis.

Log data is created in real time, so firms should deploy the available technology to pipe this data to a central server where it can be continuously monitored for suspicious activity. Security applications that are purpose-built to consume the large amount of log data produced, in all its various formats, can look for indications of attempted intrusion. If such activity is determined, alerts can be generated to security personnel to take appropriate mitigating actions.

Even with these automated log analysis tools, given the never-ending barrage of attempted attacks and their growing sophistication and complexity, cybersecurity professionals are under unrelenting stress to perform. Coupled with the industry-wide shortage of trained personnel, cybersecurity professionals are increasingly mentally fatigued, which increases the likelihood of errors or missed signals of issues requiring attention. The situation, which is exacerbated by the long hours required, is causing serious health issues for cybersecurity personnel.

To augment a firm’s cybersecurity staff, products based on the rapid developments in AI and machine learning (ML) can be deployed to assist in the analysis and identification of new attack vectors. As these application-specific AI and ML tools mature, they will play an increasingly important role in a firm’s overall cyber defense strategy.

The nature of cyberattacks has changed in recent years. No longer are attacks launched to simply bolster a hacker’s street credibility. Now it’s all about the money. No organization is safe; even hospitals and municipalities have been hit with ransomware attacks, where their data resources were held hostage until substantial funds were transferred to the cybercriminals. Financial services firms are especially preferred targets for hackers as, per the adage, “that’s where the money is.”

While comprehensive cybersecurity architectures, processes, and procedures can mitigate a multitude of vulnerabilities, firms must remain aggressive in pursuing new technology, techniques, and training to thwart the ever-changing landscape of cyber threats.

To learn more about data aggregation and artificial intelligence, and other security measures financial services firms can implement in order to mitigate the risk of cyberattacks, you can fill out the form below or click here.

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David Willner

David Willner is a business-focused information technology executive in Perficient’s financial services practice. His specialty is in transformation and data strategy programs. Before Perficient, he served as a managing director at J.P. Morgan Chase, senior managing director and chief development officer at Bear Stearns, and chief information officer, corporate comptrollers, at AIG. When he is not improving our client’s operations, systems, and data, he can be found playing guitar in his blues/rock band.

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