AI and observability: The defence against disruption

In an era where digital transformation is paramount, financial institutions stand at the forefront, constantly evolving to enhance customer experiences. However, this progress comes with increased exposure to cyber risks. In 2024, about 65% of financial institutions reported ransomware attacks, up from 64% in 2023 and just 34% in 2021. 

The complexity of the landscape is further compounded by the potential for third-party disruptions, making a resilient security framework more crucial than ever. Including third-party disruptions further complicates the landscape, underscoring the need for a dynamic and resilient security framework. This is possible by leveraging artificial intelligence (AI) to drive secure digital transformation.

Understanding the cyber threat landscape 

It’s not only rare large-scale outages that financial firms need to guard against. A recent International Monetary Fund (IMF) report warns that "global financial stability is under threat from the increasing frequency and sophistication of cyberattacks." Similarly, a World Economic Forum report highlights "growing inequalities between organizations that are cyber resilient and those that aren’t." 

Incidents in the past have caused massive system crashes, impacting millions of devices and costing the banking sector billions. This underscores the urgent need for robust cybersecurity measures to prevent such disruptions. 

The importance of observability

Observability in cybersecurity refers to the ability to monitor and interpret data from various sources to gain insights into an IT system’s health, performance and behavior. Unlike traditional monitoring, observability doesn’t just show what is happening but reveals why. 

For instance, Data Loss Prevention (DLP) offers business leaders a clear, comprehensive view of their data, including its location and access to it. It enables teams to monitor the performance of their systems, servers, and applications, allowing them to detect issues such as downtime or security vulnerabilities. Even nascent users of observability have reduced downtime costs by 90%, while industry leaders experience one-third fewer outages compared to beginners. This deeper level of insight enables quicker identification and resolution of security threats. 

Implementing observability

Implementing observability involves several key steps: 

•    Real-time monitoring: Observability solutions offer full IT ecosystem visibility, helping financial institutions detect and resolve issues early, ensuring continuous security.
•    Root cause analysis: Correlates data from multiple sources for quick problem identification, speeding up incident response and minimizing downtime.
•    Proactive threat management: AI-powered observability predicts and prevents issues, addressing vulnerabilities to reduce outages and enhance security.

The role of AI in strengthening cybersecurity

Rapid defense and deployment are crucial to mitigate risks and ensure a secure digital future, with AI playing a pivotal role. Here’s how AI can enhance cybersecurity:

•    Predictive analytics: AI leverages vast datasets to predict potential threats, enabling proactive measures against security concerns.
•    AI-driven automation: Automates tasks like vulnerability scanning, network monitoring, and behavior analysis, enabling faster threat resolution and boosting IT team efficiency by prioritizing human-critical tasks.
•    Intelligent AI monitoring: Monitors user and system behavior to detect breaches, reduce false positives and strengthen security. Enhances insider threat detection, compliance and regulatory adherence for financial institutions.

AI’s cybersecurity capabilities are interlinked, creating a comprehensive defense system that predicts and counters cyberattacks from all angles. These capabilities are bolstered by observability.

Building a resilient cybersecurity ecosystem for financial institutions

AI and observability are transforming the cybersecurity landscape, equipping financial institutions with the tools to detect and neutralize cyber threats more quickly and effectively. To keep pace with evolving cyber risks, financial firms must continuously invest in cutting-edge technologies and adopt a proactive risk management approach. By diversifying their security measures and integrating AI with observability, institutions can build robust defenses and safeguard the integrity of their digital infrastructure. 

Despite being early adopters of technology, many financial firms still rely on legacy processes that hinder resilience, putting them at a disadvantage. To build a robust cybersecurity ecosystem, institutions must diversify their security approaches and avoid over-reliance on a single vendor. 

Recommendations for strengthening cybersecurity resilience

•    Prioritize investments in advanced AI and machine learning technologies for comprehensive security solutions.
•    Adopt observability platforms for end-to-end visibility, real-time monitoring, and detailed analytics.
•    Regularly update security strategies, stay informed on emerging threats, and ensure ongoing staff training.
•    Collaborate with industry peers and cybersecurity organizations for enhanced threat intelligence.

In conclusion, AI and observability are transforming the cybersecurity landscape, equipping financial institutions with the tools to detect and neutralize threats more effectively. By embracing these technologies, institutions can ensure the integrity of their digital infrastructure and maintain customer trust in an increasingly digital world.

Financial institutions should prioritize investments in advanced AI and machine learning technologies to strengthen cybersecurity resilience, seeking comprehensive solutions for better ROI.

(Views are personal; author is Executive Vice President & Head—Europe & UKI, Financial Services, HCLTech)