Gen AI is all set to transform cloud security by redefining key capabilities: Kavitha Srinivasulu, TCS

Improving Cloud Security using Gen AI
Improving Cloud Security using Gen AI

Gen AI primarily focuses on creating the next level of evolution in automation. When machine learning and artificial intelligence are combined with cloud computing, it becomes more influential, robust, and intelligent.

This is an exclusive article series conducted by the Editor Team of CIO News with Kavitha Srinivasulu, Global Head – Cyber Risk & Data Privacy: R&C BFSI – Tata Consultancy Services (TCS).

In recent years, the digital landscape has witnessed the rapid evolution of artificial intelligence (AI), particularly in generative models. While generative models have been popularly associated with image, audio, and text generation, their application in cybersecurity is noteworthy. However, Gen AI is an emerging trend in the cybersecurity arena, going beyond predicting just images and text. While it presents an array of opportunities for enhanced security practices and controls to increase the digital transformation, it’s also a double-edged sword that can also be exploited by opponents, increasing the threats. As Gen AI techniques become more sophisticated, their application in various domains, including cybersecurity, has expanded exponentially. This article delves into the growing intersection of generative AI and cloud security in today’s threat environment.

Cloud computing has transformed the way organizations operate, manage, store, and access data. It has increased scalability and availability by increasing business resilience. However, it also brings new risks and challenges across all sectors in securing the data and maintaining cybersecurity, as cloud environments are complex, dynamic, and distinct. Gen AI helps cloud computing integrate and enhance security controls to protect cloud assets and safeguard cloud infrastructure.

GenAI is an emerging technology that helps in optimizing operational efficiency, automating security controls, prioritizing alerts, and providing actionable insights. It primarily improves productivity by designing the workflow to facilitate design optimization and enhance productivity.


GenAI is a new technology that is highly intense and designed to transform cloud security in various ways. GenAI can help address some of the most significant challenges in cybersecurity that are rising day by day, helping you to take a more proactive and predictive approach to threats, changing the reactive approach to adhering to the response plan built to handle an incident in an effective manner. By generating data using AI or ML, it allows you to expand your datasets, take a proactive approach to reduce unforeseen risks, train AI systems with more data, and gain valuable insights into new threats before they surface in a real incident. Gen AI primarily focuses on creating the next level of evolution in automation. When machine learning and artificial intelligence are combined with cloud computing, it becomes more influential, robust, and intelligent.

AI systems are still in a nascent stage, yet the technology being developed is being applied across a wide range of applications and industries. The AI industry is evolving at such a rapid pace that it is difficult for regulators to fully comprehend its implications and enact laws that address evolving concerns fast enough. For example, the release of ChatGPT in November 2022 took many by surprise due to its uncanny ability to generate coherent and contextually appropriate responses.

Significance of Gen AI in the Cloud:

  • Increased Efficiency: Automating Processes.
  • Improved Results: Generate new content, ideas, and solutions to help in decision-making.
  • Data-Driven: Provides data-driven insights and predictions to enable controls in the right place.

Gen AI uses machine learning technology to optimize results by exploring more possibilities than traditionally humanly possible results. It helps in creating new and original content, whether it be images, music, text, or even entire virtual worlds, using advanced machine learning techniques such as deep learning and neural networks. The integration of AI and cloud security has demonstrated its effectiveness in threat identification, risk prioritization, and anomaly detection. As organizations have started using the cloud as the base of connectivity to access data or applications, AI has emerged as a valuable partner, enabling enhanced protection of sensitive information, strengthening privacy and security measures, and proactively mitigating unwanted and malicious activities executed by predators or cyber hackers., all with minimal human intervention.

The integration of Gen AI into cloud-based security introduces some new cybersecurity challenges that organizations need to focus on. The huge processing and storage of data within the cloud is a very critical area, as it is prone to risks and has chances to rise in securing data and aligning with compliance requirements. To mitigate these risks, organizations must implement robust encryption protocols and impose stringent access control mechanisms to safeguard their cloud environments and ensure the protection of sensitive data.

Challenges in adapting to Gen AI

  • Availability and quality of data—to avoid getting misled by the information provided by the GenAI Tool.
  • Scalability: Training complex generative AI models often requires significant computational resources. Scaling up the training process can be expensive and requires investment.
  • Identifying the source: It does not always identify the source of the content.
  • Trust: It is difficult to trust without the right source of identity.

GenAI in the cloud can address challenges related to threat detection, incident response, compliance, resource constraints, and the dynamic nature of cloud environments. However, enabling the right set of security controls for securing data while applying GenAI will benefit the business by increasing productivity and advancing data security. Gen AI focuses on creating and designing new content rather than simply analyzing existing data or making predictions based on the patterns. While standard AI techniques like machine learning and deep learning are used for classification and prediction tasks that work based on predefinition.

Some of the key use cases of GenAI in the cloud are:

Organizations visualize using Gen AI in the cloud security model that it can easily translate from one language to another—speech or machine language—which includes translating natural language queries into the vendor-specific languages needed to conduct the search in other tools. Gen AI will be programmed to handle investigations more efficiently as well as prioritize which alerts should be handled first to proactively stop the incident before it occurs.


Generative AI can recommend next steps using chatbots to provide responses about policies, procedures, or best practices by advancing the searches and providing robust results. Eventually, organizations will start applying the GenAI approach to the cloud to use their own security data and threat pattern recognition capabilities to create predictive threat models.

Benefits of Gen AI in Cloud Security:

Gen AI is changing the old pattern of identifying and building the key security capabilities of cloud security. It helps in changing the old pattern of identifying the data by proactively addressing the challenges in detecting the vulnerabilities in the current environment. Some of the key benefits of Gen AI in cloud security are as follows:


Gen AI can be applied across all aspects of the cloud to secure data. Its ability to automatically generate new content and design complex data while interpreting and identifying the type of data with the provided content is a game-changer. Applying Gen AI to the cloud saves processing time by managing the governance model and reducing human intervention. Provide insights and predictions based on massive data analysis, enabling data-informed decision-making to adapt to a proactive approach and reduce threats.

Gen AI in the cloud is a robust technology for enhancing the security of cloud environments in the face of modern cyber threats and challenges. Applying this technology to the cloud will help organizations proactively detect, prevent, and respond to cyber threats while reducing operational burdens and optimizing operational efficiency. To sum up, Gen AI has the potential to significantly enhance business processes, leading to increased competence, improved productivity, raised customer satisfaction, and cost-efficiency to advance cloud security posture.

Gen AI in cloud security is essential and helps to improve IT security performance at an intense level. It brings easy analysis and threat identification, which helps security professionals reduce cyber incidents, data breaches, direct incident response, and identify malware attacks before they occur. This integration of Gen AI in the cloud holds immense promise, revolutionizing operations and customer experiences in today’s digital era. From ensuring regulatory compliance and transparent AI utilization to robust security measures, data governance, and ongoing collaboration between Gen AI and the cloud for successful adoption by continuing continuous monitoring, customer education, and iterative cloud security implementation, enhancing the current transformative technology and emphasizing the need for increasing cyber and business resilience.

About Kavitha Srinivasulu

Kavitha Srinivasulu is an experienced cybersecurity and data privacy leader with over 20 years of experience focused on risk advisory, data protection, and business resilience. She has demonstrated expertise in identifying and mitigating risks across ISO, NIST, SOC, CRS, GRC, RegTech, and emerging technologies, with diverse experience across corporate and strategic partners. She possesses a solid balance of domain knowledge and smart business acumen, ensuring business requirements and organizational goals are met.

Disclaimer: The views and opinions expressed by Kavitha in this article are solely her own and do not represent the views of her company or her customers.

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