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How Artificial Intelligence is Redefining Cybersecurity

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Hetal Presswala, Chief Information Security Officer, Kalpataru Limited

Hetal Presswala is a technology leader with cyber security breach experience, driving confidentiality, integrity, and availability of enterprise information systems. A visionary who translates evolving industry risks into ambitious technology roadmaps and robust security programs.

In a conversation with Keerthana H K, Correspondent, CIO Insider Magazine, Hetal Presswala, Chief Information Security Officer at Kalpataru Limited, shared his views and thoughts pertaining to AI-driven cybersecurity.


What are the common challenges that organizations face in the AI-driven cybersecurity field, and how can these be mitigated?
Common challenges and limitation are

•False positives and negatives:
o Challenges: AI-powered cybersecurity systems can generate false positives, flagging legitimate activities as suspicious or false negatives, missing actual threats. This can lead to wasted time and resources, and create a sense of complacency if organizations start to ignore false positives.

o Mitigation: Continuously refine AI models with feedback from security experts. Implement feedback loops and mechanisms to reduce false alarms and ensure that the system adapts to evolving threats.

• Adversarial attacks:
o Challenges: Adversarial attacks are where malicious actors manipulate inputs to deceive or exploit AI algorithms. For example, an attacker might create a slightly modified version of a known malware sample to evade detection by an AI-powered antivirus system.

o Mitigation: Organizations should train their AI-powered cybersecurity systems on data of the latest threats and attack techniques. Organizations should also use techniques such as adversarial training to make their systems more resilient to adversarial attacks. Keep models up to date to defend against evolving attack techniques.

• Data Quality and Availability:
o Challenge: AI models require high-quality, labeled data for training. In some cases, organizations may not have access to enough data or may have data that is incomplete or biased.

o Mitigation: Invest in data collection and quality assurance processes. Augment your data with external sources if necessary. Additionally, consider using data augmentation techniques to generate more diverse training data.

• Interoperability and Integration:
o Challenge: Integrating AI-driven cybersecurity solutions with existing security infrastructure and tools can be complex.

o Mitigation: Plan for seamless integration by using open standards and APIs. Consider leveraging security orchestration and automation platforms to coordinate actions between different security tools.

• User Acceptance:
o Challenge: Security teams and end users may be skeptical of AI-driven solutions and resistant to their adoption.

o Mitigation: Provide training and education to build trust in AI systems. Demonstrate the value and

effectiveness of these solutions through pilot projects and real-world success stories.

• Ethical and Privacy Concerns:
o Challenge: AI can raise ethical issues, especially when it comes to privacy and biases in decision-making.

o Mitigation: Develop and adhere to ethical AI principles. Ensure compliance with relevant regulations like DPDP, GDPR and establish transparent data handling and model explain ability practices.

Addressing these challenges requires a well-thought-out strategy and ongoing vigilance. Organizations should be prepared to adapt and evolve their AI-driven cybersecurity initiatives as the threat landscape and technology continue to change.

What steps could ensure that AI-based cybersecurity systems are properly integrated into an organization's existing security infrastructure and processes?
To ensure that AI-based cybersecurity systems are properly integrated into an organization's existing security infrastructure and processes, the following steps should be taken:

• Assess the organization's current security posture and identify the areas where AI-based cybersecurity systems can be used to improve security. This includes identifying the organization's most critical assets, the types of threats it faces, and the gaps in its existing security infrastructure.

• Select AI-based cybersecurity solutions that are aligned with the organization's security needs and goals. There are a variety of AI-based cybersecurity solutions available, so it is important to select solutions that are appropriate for the organization's size, industry, and risk profile.

AI-based cybersecurity initiatives can help to automate tasks and reduce the need for manual intervention, which can lead to a reduction in the cost of cybersecurity operations.

• Develop a plan for integrating the AI-based cybersecurity solutions into the organization's existing security infrastructure and processes. This plan should include how the data from the AI-based systems will be integrated with other security data and how the AI-based systems will be used to inform and automate security decisions.

• Implement the AI-based cybersecurity solutions and monitor their performance over time. It is important to monitor the performance of the AI-based systems to ensure that they are detecting and responding to threats effectively.

• Train and educate employees on how to use the AI-based cybersecurity systems. This will help employees to understand how the systems work and how to use them to improve their own security practices.

What metrics and key performance indicators could measure the success and effectiveness of AI-based cybersecurity initiatives in the organization?
The following metrics and key performance indicators (KPIs) can be used to measure the success and effectiveness of AI-based cybersecurity initiatives in an organization:

• Reduction in the number of security incidents: This is a direct measure of the effectiveness of AI-based cybersecurity initiatives in preventing and detecting cyberattacks.

• Reduction in the time to detect and respond to security incidents: This metric measures the effectiveness of AI-based cybersecurity initiatives to help organizations quickly identify and respond to cyberattacks.

• Improvement in the accuracy of threat intelligence: This metric measures the effectiveness of AI-based cybersecurity initiatives in helping organizations to understand the latest threats and attack techniques.

• Reduction in the cost of cybersecurity operations: AI-based cybersecurity initiatives can help to automate tasks and reduce the need for manual intervention, which can lead to a reduction in the cost of cybersecurity operations.

• Improvement in employee security awareness: AI-based cybersecurity initiatives can help to educate and train employees on security best practices, which can lead to an improvement in employee security awareness and a reduction in the risk of human error.

By using the right metrics and KPIs, organizations can track the progress of their AI-based cybersecurity initiatives and identify areas where improvement is needed. This will help organizations to ensure that their AI-based cybersecurity initiatives are effective and that they are helping to protect the organization from cyberattacks.

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