Role of Emerging Technologies like AI/ML in Strengthening the Cyber Security Framework

Knack of AI/ML in Cyber Security Framework

As organizations are advancing in their journey of digital transformation, they are also falling prey to more developed and sophisticated threats. According to the Cyber Attack Trends: 2021 Mid-Year Report by Checkpoint, there has been a dramatic increase of 93% in ransomware attacks globally in the first half of 2021. The organizations in the APAC region have witnessed 1338 weekly attacks, a 13% increase in cyber-attacks. In such a scenario, the traditional approach of human-dependent cyber defense is no longer viable when compared to the intelligence and growth of the present-day risks. Thus, the integration of emerging technologies like Artificial Intelligence and Machine Learning into the security framework becomes essential for enabling risk mitigation, early prevention of minor intrusions from escalating into compromises, and protection of critical business assets. An intelligent security approach includes advanced data encryption, hashing, tokenization, and key management practices to protect information across the organization.

Solutions enhanced with AI/ML simplifies the protection of vulnerable data even in the most crucial and complex situations. It helps in personalizing the mitigation approach depending on the business requirements, real-time threat identification, analysis, and prevention. An AI based cybersecurity system relies on the continuous data flow to filter patterns and backtrack the attacks for smarter protection.

Types of cyber security threats

A report Indian Computer Emergency Response Team (CERT-In) found that 3,137 cyber security-related issues were reported every day during 2020. In today’s digitally advanced world, organizations are being exposed to a newer generation of threats and attackers who deploy a wide range of attack types for gaining unauthorized access and cause business implications. These threats are constantly evolving in terms of scale, complexity, and sophistication to compromise data confidentiality, integrity, and availability. Cyber-threats can be broadly classified into three categories basis the intention:

• Attacks on confidentiality, devised for accessing vulnerable and sensitive data
• Attacks on integrity designed to sabotage business operations and cause reputational damage, and
• Attacks on availability to restrict users from accessing information

Cyber-attacks at an individual level are aimed at identity theft, extortion, and loss of unique data. Cyber-attacks at an organizational level results in data breaches, business discontinuity, demands for ransom, industrial espionage, reputation loss and regulator censure. Depending on the analysis of the potential threat, an integrated and automated approach to strengthening cyber resilience must be adopted to accelerate the prevention, detection, and remediation of cyber breaches.

Impact of technologies to the development of hack-free digital environment

The rapidly advancing threat landscape demands the constant progression of cybersecurity capabilities. The Micro Focus report - 2020 State of Security Operations, found that over 93% of respondents’ organizations use AI/ML based security operations products to focus on threat detection capabilities. As data volumes grow, computing power increases, and internet resources expand, AI/ML protects sensitive data and keeps threat actors away from the internal networks. AI-based anti-spam, firewall, intrusion detection/protection, and other cyber security systems are new-age tools that go beyond the archaic strategy of risk mitigation.

The key to ensuring a hack-free digital environment is by strengthening the cyber resiliency framework of the business. A comprehensive strategy to assure cyber resilience involves the implementation of cybersecurity throughout the enterprise lifecycle for protecting the business, detecting the changing risk landscape, and evolving capabilities to address the changing threats. The cyber resiliency efforts progress through seven stages of assessment – strategize, withstand, defend, inspect, observe, recover, and adapt. Over the years, we at Micro Focus have been empowering our customers with one of the largest security portfolios offering innovative solutions such as ArcSight, Fortify, Interset, NetIQ, and Voltage. These solutions deploy AI/ML capabilities to analyze data, identify patterns and behavior, and automate response to enable intelligent adaptation to vulnerabilities and attacks.

Future viewpoints for AI/ML in Cyber Security Framework

There is enormous potential for Artificial Intelligence and Machine Learning in strengthening the threat detection capabilities. Integration of security analytics combined with new data types result in new and evolved security frameworks for analyzing and mitigating breaches. It is essentially the application of battle-tested algorithms and methodologies for simpler and agiler risk mitigation. It aids security frameworks with the ability to operate sophisticated analysis on huge amounts of data with minimum complexity and provides dexterity to quickly move from investigation to action. This allows security operations teams to analyze the impact and minimize the effect of breaches in real time. In future, AI and ML will not wait for an attack to happen but work on predicting an attack based on history and suggest solutions to fight the threat. Moreover, this will work without human intervention, and hence the need for physical resources to monitor the network is not required 24×7. Therefore, it will enable enterprises to save time and expenses of hiring cybersecurity experts in large numbers.