Ram Narayanan, Country Manager at Check Point Software Technologies, Middle East, highlights the growing importance of proactive cybersecurity measures in the region. He emphasises the need for organisations to adopt advanced threat detection tools, leverage AI-driven solutions, and implement robust security frameworks to combat the increasing complexity of cyber threats

How is generative AI being utilised to enhance cybersecurity measures today?
Generative AI is revolutionising cybersecurity through automated threat detection, security operations and decision-making. Check Point Software embeds GenAI throughout its solutions to enhance efficiency and accuracy. The Check Point Infinity AI Copilot speeds up security management by automating policy generation, threat analysis and incident response, cutting task resolution time up to 90%. Check Point Infinity GenAI Protect guarantees the safe adoption of generative AI use cases by monitoring shadow AI use, blocking data leaks and ensuring compliance with regulations. Through AI-driven threat intelligence, Check Point further enhances its capacity to detect new threats, block phishing and malware attacks and offer real-time security insights. These solutions enable organisations to pre-emptively fortify their cyber defenses while having complete visibility and control over their AI-powered security environment.

What potential risks does generative AI introduce in the cybersecurity landscape, such as AI-driven cyberattacks?
While generative AI offers tremendous progress, it also presents new security threats, such as AI-powered cyberattacks, data leakage and shadow IT issues. Cyber attackers can use AI to automate and amplify social engineering attacks, create advanced phishing emails and produce deepfake material that conventional security solutions might find difficult to detect. Moreover, data spillage is of significant concern as workers may unwittingly feed secret or copyrighted information into public AI models, who in turn might be utilised to train subsequent AI systems. Traditional data loss prevention (DLP) products usually fall short as they depend on pre-established patterns and lack understanding of the contextual nature of unstructured, chat-like data prevalent in GenAI interactions. Without adequate visibility and governance, organisations can lose sensitive information and open themselves up to compliance breaches and security risks.

How can organisations leverage generative AI for proactive threat detection and response?
Organisations can utilise generative AI for real-time threat detection, incident response automation and improved security governance. Check Point’s Infinity GenAI Protect enables enterprises to discover, assess and secure GenAI applications within their environment, providing AI-powered data classification to prevent sensitive information from being leaked. Through the implementation of context-aware security controls, it ensures that AI-driven tools can be adopted securely without compromising critical data. Further, ThreatCloud AI constantly processes telemetry data and indicators of compromise (IoCs) to identify and neutralise phishing, malware and zero-day attacks in real time before they strike. Security operations teams also make use of Infinity AI Copilot, which automates incident response, policy compliance and threat hunting, shortening the time they spend on such manual efforts so that they can concentrate on high-level security strategies.

What ethical concerns arise when using generative AI in cybersecurity, and how can they be addressed?
Generative AI brings with it ethical issues like data leakage, absence of visibility and risk for compliance. The problem is that GenAI tools tend to be shadow IT, so administrators don’t even know about their use. Without governance in place, organisations are at risk of leaking sensitive or copyrighted information, as GenAI services can utilise user inputs for training models. Legacy DLP solutions are not effective in dealing with unstructured, conversational data, adding to the risk of confidential information leakage. To meet these challenges, organisations require AI-driven data analysis that effectively classifies conversational data, offering visibility into GenAI usage, data leakage prevention, and compliance with regulatory standards. Check Point’s methodology is centered around providing AI-driven solutions that can facilitate safe adoption of GenAI, allowing business organisations to achieve its advantages while not creating any new security exposure.

What challenges do cybersecurity teams face when integrating generative AI tools into their workflows?
Incorporating generative AI into cybersecurity involves challenges like managing data quality, false positives and workforce acclimatisation. Security products based on AI need high quality, real-time threat intelligence to be effective. If AI models are trained using biased, out-of-date or incomplete data, they will not be able to identify emerging threats or raise false alarms, wasting precious resources. Security professionals also need to adjust to workflows that include AI, which entails training and reskilling. Check Point neutralises these problems with Infinity ThreatCloud AI, which consolidates high-quality, real-time threat intelligence from 150,000 networks and millions of endpoints to enhance AI accuracy. Infinity AI Copilot also eases AI deployment by automating administrative tasks, simplifying complexity and offering AI-guided directions, enabling security teams to adopt AI seamlessly in their operations.

Are there any notable examples of generative AI successfully preventing or mitigating cyberattacks?
Check Point has included Generative AI in its security products to increase threat prevention and response. Infinity AI Copilot uses Generative AI to automate intricate security tasks and minimise response time, as well as increase accuracy in threat mitigation. It helps security professionals by automating policy design, incident investigation and threat analysis and reducing resolution times by 90%. In addition, GenAI Protect provides secure adoption of generative AI solutions by identifying shadow IT threats, blocking data breaches and imposing governance rules. Together with ThreatCloud AI, these products offer real-time threat intelligence and proactive protection against AI-driven cyberattacks. By incorporating Generative AI into security processes, Check Point enables organisations to block sophisticated threats before they can execute.

How do you see generative AI evolving in the cybersecurity domain over the next few years?
Generative AI will become increasingly critical to cybersecurity, facilitating more sophisticated threat detection, predictive analytics and automated response capabilities. AI-based security tools will continue to advance, enabling organisations to detect and neutralise sophisticated cyber threats in real time. The convergence of AI with zero-trust architectures will strengthen identity verification and anomaly detection. As AI becomes more powerful, it will be better at defeating AI-generated cyberattacks. Moreover, regulatory structures will adapt to promote responsible use of AI, striking a balance between automation and human intervention to preserve accuracy and security in a constantly shifting threat environment.

What role does human oversight (HITL) play in ensuring generative AI systems are effectively managing cybersecurity threats?
Human oversight is required in AI-driven cybersecurity to ensure accuracy, ethical decision-making and appropriate response to threats. While AI may detect patterns and automate response, human experience is required to validate AI-generated insights and analyse complex threats. AI models must be updated and monitored periodically to prevent biases, misclassifications or adversarial attacks. Security teams play a critical role in training AI with good data and making strategic decisions based on AI suggestions. A balanced approach, where AI assists security efficiency and humans provide oversight, ensures that AI is a reliable tool and not an uncontrolled decision maker.

How can smaller organisations with limited budgets incorporate generative AI for cybersecurity?
Smaller organisations can fortify their security stance by implementing scalable AI-powered solutions such as Check Point’s Infinity AI Copilot, offering enterprise-grade security without the necessity of large-scale in-house security infrastructure. Cloud-based AI security platforms provide economical threat detection, real-time monitoring and automated response features. AI automation lightens the load for small security teams by performing routine security tasks, enabling staff to concentrate on priority threats. Putting emphasis on AI-based endpoint protection, phishing protection and network scanning allows smaller companies to protect themselves against cyberattacks without enormous expenditure.

What best practices would you recommend for implementing generative AI tools while minimising risks?
To deploy generative AI securely, organisations must first have visibility into how AI is used within their environment to know what the threats could be. Defining governance policies clearly protects the security and compliance that AI tools must support. AI-driven data classification is critical to stopping data leaks because traditional DLP solutions have difficulty with the contextual nature of GenAI prompts. To further reduce risks, companies should institute access controls to govern how employees use GenAI tools and block unauthorised data exposure. Continuous monitoring and real-time threat detection enable identifying and mitigating security vulnerabilities prior to their exploitation.