The cyber threat landscape has been evolving continuously.
The COVID-19 pandemic exacerbated existing security weaknesses and undermined security readiness. In response to the pandemic, many organizations rapidly adopted cloud, BYOD, and VPN solutions, creating new attack surfaces such as endpoint security, cloud security, and identity-based breaches. Cyber-attacks on enterprises have resulted in damage to the company's brand, compliance, assets, and business continuity. Loss of customer trust often leads to customers defecting to competitors, causing stock prices to drop and shareholder value to decline. The ongoing threat of denial-of-service attacks only adds further strain on enterprise operations.
Currently, the multitude of point products and technologies employed for cyber security purposes such as detection, protection, and remediation are proving to be inadequate and cluttered, leading to widespread cyber attacks. Rather than a streamlined system, the security landscape is plagued by chaos, referred to as a "Security Mess". The existing and emerging technologies generate security data in various forms, both structured and unstructured, from multiple sources, resulting in data overflow. The lack of a comprehensive, enterprise-wide view of security, compliance, and response exacerbates the problem, creating what is referred to as a "Visibility Mess". When considering the infrastructure of an enterprise, the challenge is compounded by factors such as diversity, volume, and speed of data, as well as the reliance on traditional rule-based engines or analyst-driven incident detection, resulting in slow and ineffective responses.
Is there a more effective solution?
Cyber-attacks utilizing AI are becoming increasingly sophisticated, making it necessary for enterprises to adopt cutting-edge AI-based technologies and services to ensure the protection, prevention, detection, response, and mitigation of these attacks. By doing so, they can replace or enhance their outdated legacy technologies.
According to Gartner, the fastest-growing areas in cybersecurity are those utilizing AI for integration, attack prioritization, and remediation.
The Advantages of Implementing AI in Cybersecurity
Rapid Data Processing Regardless of Volume: CISOs can utilize Cognitive AI's capabilities to swiftly examine Indicators of Compromise and gather crucial information displayed on an interactive real-time dashboard.
Threat Detection, Known and Unknown: AI algorithms possess the ability to self-analyze attack patterns and behaviors to detect and counter advanced threats. CISOs can access relevant analytics and benchmarking to make informed decisions.
Reduced False Positives and Noise: The AI algorithms provide improved accuracy, reducing the number of false positives. This helps to alleviate alert fatigue among security teams, minimize analyst workload, and save valuable time, resulting in a faster mean time to respond and resolve.
Accelerated Detection and Response: Technologies like SOAR, mapped to the MITRE ATT&CK framework, use TTP to automate and integrate time-consuming security response actions that previously required human intervention.
Ensuring Business Continuity: The implementation of autonomous AI-based threat-hunting capabilities guarantees business continuity.
The adage "Prevention is better than cure" rings true in the world of cybersecurity. The integration of AI-based technologies into security measures will shift the focus from reactive to proactive, preventing the devastating consequences of cyber-attacks. Implementing AI in a comprehensive security architecture not only leads to a reduced total cost of ownership and increased return on investment but also ensures compliance. Gone are the days when AI-based cybersecurity was a mere luxury; it is now a crucial necessity for businesses, as the consequences of neglecting it will prove costly.