In an era where cyber threats evolve at breakneck speed, organizations are turning to artificial intelligence to strengthen their security posture. Ravindra Ramnani, Senior Manager of Solutions Architecture at Elastic, offers a glimpse into how AI-powered search is revolutionizing threat detection and incident response. In this exclusive interview with CIO&Leader, Ramnani explains how generative AI and retrieval augmented generation are transforming security operations from reactive damage control to proactive threat prevention. As businesses navigate complex regulatory landscapes and hybrid cloud environments, Elastic’s innovative approach demonstrates how organizations can harness the power of AI while maintaining data security, compliance, and resilience against emerging cyber risks.
CIO&Leader: How is AI-powered search transforming threat detection and incident response, and what role does generative AI play in security analytics?
Ravindra Ramnani: From an organizational perspective, generative AI (GenAI) can automate threat responses, reduce investigation time, and provide actionable insights. It can boost penetration testing efforts, where cybersecurity teams attempt to discover and exploit vulnerabilities in their IT systems. This exercise lets security teams identify any weak spots that bad actors can exploit. GenAI can create customized exploits for the organization’s IT systems, allowing security teams to identify even more system vulnerabilities and receive real-time feedback on remediation strategies.
Security teams can quickly identify root causes, predict emerging threats, and streamline response actions without manually sifting through massive amounts of security data. This shift allows organizations to move from reactive threat management to proactive security operations.
GenAI can be an even more powerful tool by being provided with proprietary data. Elastic supports this transformation by being the bridge between GenAI and private data. The Elastic Search AI platform ingests all data from any source, including custom knowledge sources, providing a unified foundation for finding, analyzing, and using data in real time.
Elastic Security, built on the Search AI platform, uses Search AI Lake architecture to allow seamless and advanced analysis of relevant data, even from years past. Organizations can find and use the correct data at the right time, modernizing their security operations.
Additionally, AI and ML help reduce false positives, which ensures security teams aren’t overwhelmed by irrelevant alerts. Elastic Security also streamlines alert triage workflows by assessing multiple recent alerts in the environment and informing security teams of alerts that require their immediate attention. These abilities significantly optimize threat detection processes and enhance security operations.
CIO&Leader: Large Language Models (LLMs) can generate misleading or biased results. How does Elastic ensure accuracy, relevance, and Security in AI-driven searches for cybersecurity use cases?
Ravindra Ramnani: Effective threat detection and response require precise and contextually relevant insights. However, traditional LLMs have inherent limitations—they cannot retain new information post-training and may generate outdated, biased, or misleading responses. Retrieval Augmented Generation (RAG) is a method that optimizes an LLM’s responses by retrieving information from private or proprietary data sources. It enhances LLMs by combining information retrieval with GenAI, ensuring that responses are grounded in accurate, up-to-date data rather than relying solely on pre-trained knowledge.
From a security perspective, RAG improves threat intelligence by dynamically integrating the latest security intelligence, generating comprehensive threat assessments from multiple data sources, and providing real-time updates on emerging cyber risks. It also boosts incident response and analysis by rapidly retrieving relevant historical incident data, allowing security teams to adapt responsive strategies and learn from previous security events.
RAG enhances cybersecurity workflows by allowing security teams and developers to leverage real-time threat intelligence rather than relying solely on static AI models. It continuously updates search results with the most recent attack patterns, vulnerabilities, and compliance requirements, helping organizations make informed security decisions. By grounding AI-driven responses in verified, up-to-date data, RAG reduces misinformation, enhances detection accuracy, and supports faster incident response.
Elastic incorporates RAG within its Search AI platform to provide security teams with contextual, real-time insights that improve threat detection and investigation. This approach eliminates the need for frequent retraining of LLMs, ensuring security operations remain agile and efficient. Additionally, Elastic prioritizes data security by offering on-premises and private deployment options, allowing organizations to maintain complete control over their security data while adhering to compliance regulations.
CIO&Leader: With regulations like India’s DPDP Act and GDPR, how does Elastic balance AI-driven search efficiency with stringent data security and compliance requirements?
Ravindra Ramnani: Elastic is committed to ensuring that search remains robust and compliant with global data protection laws, including India’s Digital Personal Data Protection (DPDP) Act and the General Data Protection Regulation (GDPR). By embedding Security, privacy, and governance into its platform, Elastic enables organizations to harness AI-driven insights without compromising compliance.
Elastic incorporates robust access controls such as role-based and attribute-based access to meet these stringent regulations, ensuring that only authorized users can access sensitive data. Data encryption, both at rest and in transit, further enhances Security. At the same time, data minimization and pseudonymization techniques allow organizations to process and store data securely, reducing exposure to personally identifiable information.
Elastic also provides flexible deployment options, including on-premises and private cloud solutions, giving organizations complete control over their security data. By integrating compliance-driven features into its Search and AI capabilities, Elastic empowers businesses to drive innovation while safeguarding privacy and adhering to regulatory standards.
CIO&Leader: What are the key security challenges in integrating AI-powered search across hybrid cloud environments, and how does Elastic address them?
Ravindra Ramnani: Organizations often struggle to secure distributed environments, monitor sensitive data in real-time, and ensure compliance with evolving regulations. Integrating search technologies across hybrid cloud environments presents significant security challenges, including fragmented security policies, inconsistent visibility, and complex access management. These gaps create vulnerabilities that cyber threats can exploit, increasing the risk of breaches and compliance violations.
To address these challenges, Elastic Security provides a unified security platform that delivers real-time visibility across hybrid and multi-cloud environments. Organizations can proactively detect and mitigate security risks across distributed cloud infrastructures by leveraging agentless cloud-native threat detection, automated misconfiguration remediation, and workload protection. Elastic also enables deep contextual visibility by integrating security data from multiple cloud services, helping businesses enforce consistent security policies and prevent unauthorized access.
As cyber threats grow more sophisticated, organizations require a proactive approach to Security. Elastic’s modern security information and event management (SIEM) and Search AI capabilities empower security teams with real-time anomaly detection, behavioral analytics, and automated response mechanisms. By enabling extended protection for endpoints, cloud workloads, and IT environments, Elastic ensures that businesses can defend against evolving cyber risks while maintaining compliance with stringent data security regulations.
CIO&Leader: How does Elastic safeguard its AI-powered search solutions from manipulation, adversarial attacks, or search results poisoning?
Ravindra Ramnani: As Search and AI become integral to cybersecurity, safeguarding them against manipulation, adversarial attacks, and data poisoning is critical to maintaining trust and operational integrity. Organizations face evolving risks such as adversarial attacks, model poisoning, prompt injection, and data bias, which can distort search results, introduce security vulnerabilities, and weaken overall defenses.
To mitigate these risks, security teams must first identify and contextualize threats within broader cybersecurity frameworks. Elastic’s InfoSec risk management program is built around the FAIR (Factor Analysis of Information Risk) model to quantify AI-related threats, enabling organizations to assess potential risks in measurable terms and build more resilient security strategies. By embedding AI governance principles, businesses can protect against manipulation while ensuring transparency and accountability in AI-driven security operations.
Elastic addresses these challenges through RAG by ensuring that AI-driven search results are grounded in verified, up-to-date data, preventing adversaries from injecting misleading or manipulated content. Real-time anomaly detection and behavioral analytics further enhance Security by identifying suspicious patterns early, allowing teams to mitigate threats before they escalate.
Beyond detection, Elastic safeguards its AI models through strict data validation processes, AI governance controls, and continuous monitoring. By integrating compliance-driven security mechanisms, Elastic enables organizations to leverage Search AI confidently while ensuring protection against evolving cyber risks. As AI technologies advance, Elastic remains committed to adaptive security measures, reinforcing trust and reliability in AI-driven cybersecurity solutions.
CIO&Leader: Looking ahead 3-5 years, how do you see AI-powered search evolving in cybersecurity, and what innovations are Elastic working on to stay ahead?
Ravindra Ramnani: Over the next three to five years, search and AI will play a transformative role in cybersecurity, enhancing threat detection, automating incident response, and strengthening compliance. As attackers increasingly exploit exposed credentials and misconfigured cloud environments, cyber threats are growing more sophisticated. Emerging tactics, such as advanced trojans, crypto mining malware, and double extortion ransomware, are evolving alongside stealthy, long-term attack campaigns. These trends underscore the need for intelligence-driven cybersecurity strategies focusing on proactive threat detection, robust access controls, and adaptive security frameworks to stay ahead of evolving risks.
Elastic is driving this evolution by transforming SIEM with AI-driven security analytics. Leveraging Search AI, Elastic enhances investigation and response, delivering precise, context-aware security insights. Its AI Assistant, powered by the Elastic Search AI Platform, unifies security data, enabling real-time anomaly detection and automated threat mitigation.
Looking ahead, Elastic remains committed to continuous AI innovation, helping organizations anticipate, adapt to, and defend against emerging cyber threats with advanced, resilient security solutions.