Hello, my name is Sanjay. I'm honored to be part of con, so today I'm presenting integration cloud computing. So especially challenges, solutions and ethical frameworks. So the fusion of artificial intelligence with cloud computing is transforming industries globally, creating unprecedented opportunities and challenges. So with AI cloud market growth, reaching 11.2 billion in 23 and growing at 35.6% annually. So organizations are rapidly adopting these technologies. So this presentation explores the key challenges ordinations face when integrating AI with cloud systems provides evidence-based solutions to overcome these barriers, and emphasize the importance of ethical frameworks in ensuring sustainable growth, the key challenges of our secondary challenges in AI cloud system. The security stands as the foremost obstacle to widespread a adoption in cloud environments. So, yeah. Yeah, allowing 18% of our nations have experienced SA security instance while 78% struggle with data privacy vulnerabilities, particularly when sensory information is processed by AI systems in cloud. So the risk landscape is further complicated by. The fact that 71% of our nation lack sufficient automated securely tools to address these threats effectively. So the model production emerges as another critical vulnerability with 65% of our nations reporting concerns. These valuable AI assets become suspectable to theft, reverse engineering, and sophisticated adverse attacks when deployed in cloud environments. Meanwhile, 42% of our nations face complaints. Violence is highlighting that, regulatory challenges that the company AA cloud integration. So coming to. Solution advanced production mechanisms. So with the mplementation of homomorphic encryption enables AI systems to process fully encrypted data without decryption preserving privacy toward the entire computational pipeline. So this breakthrough technology has demonstrated a 92% direction in privacy breaches violating sensor data in ex exposure duty. Processing cycles. The next secure entails establishes hardware isolated execution environments for AI workloads, safeguarding both proprietary models and the data being analyzed. So organization implementing secure and clear architecture report. And exceptional 99.99% effectiveness rate in defending air models diagnos against sophisticated adverse attacks. So with federated learning facilitators, distributed model training across decentralized device without consolidating since two data in cloud repositories. So this innovative approach has decreased data vulnerability exposure by 87%. While maintaining model performance within 3% of traditional centralized training methodologies. So these sophisticated security frameworks directly addresses the most significant vulnerabilities in AI cloud integration ecosystem, empowering automations to deploy advanced data systems with enhanced confidence under streamlined regulatory compliance. The next challenge, operational complexities in a cloud systems. So, so the operate op operational complexity, especially with the integration challenges. So 82% of enterprise and quantums sustainable technical debt when matching legacy systems with cutting edge, a cloud zone resulting. In extended timelines and compromise scalability potential, the next market, 64% of organizations face significant hurdles in tracking AI model performance across distributed cloud environments, so leading to diminish service quality and undetected drift over time. The next deployment barriers seven 3% of DevOps teams in. And maintaining consistent deployment pipelines for AI workloads in complex multi-cloud ecosystems. So, so the operational complexities are orchestrated AI within cloud environments, transfer and initial implementation hurdles. Organizations must navigate ongoing integration challenges, sophisticated monitoring requirements, and deployment complexities. That collectively have the full realization of as transformative potential in business operations. So the operational solution, streamlining a AI cloud integration. So implementing service mesh just delivers a 92% implement in service discovery, liability, and reduces interservice communication. LA Agency by 78. For AI workloads. So this sophisticated approach establishes a dedicated infrastructure layer that top images and secure all service to service communications within AI cloud ecosystem. The next monitoring automations, leveraging AI systems to monitor the air deployment experience. A 43% reduction in operational cost alongside 67% faster anomaly detection. So this intelligent meta AI system practically identify performance degradation and model drift patterns before they impact production environments on end users. Next. Infrastructure As Code ISC, the Enterprise Enterprises implementing ISC pathologies for AI deployments achieve 1800% faster provisioning cycles and reduce configuration errors by 64%. So this programmatic approach enables consistence, reproducible, and virtual control deployments across heterogeneous cloud environments, eliminating manual configuration inconsistencies. So these strategic operational solutions directly address the fundamental challenges of managing AA within cloud ecosystems. Empowering organizations, so, uh, empowering organizations to achieve unprecedented level of reliability, operational efficiency, and seamless scalability throughout their AA deployment lifecycle. The next ethical considerations in EA cloud company computing, especially there are some fairness. The transparency. Transparency in addition, making the environmental impact, the privacy of sense to get out Accountability. So the ethical dimensions of air deployment in cloud environments transfer via technical considerations, so organizations that neglect robust ethical frameworks, risk implementing systems that discriminate against vulnerable populations, compromise personal privacy or function as black. IMP black boxes. That meaningful scrutiny. Addressing these ethical challenges represents not only a moral imperative, but increasingly a strategic business necessity. As regulatory landscapes evolve on stakeholder expectations, mature organizations that prioritize responsibly a practices gain complicated advantages. Through enhance trust, reduce reduced competencies and sustain social license to operate. So coming to ethical Solutions framework for responsibility. So automation, implementing systematic bias testing across diverse demographic groups have achieved percent in metrics on 56% reduction financially. Discriminate outcomes, an extra. The expandable ai, the implementation of cybersecurity XIA techniques has an enhanced model transparency by 87% and elevated user trust by 64%. Right? So ethical review boards, the company's established dedicated cross-functional a ethics committees demonstrate net 0.8% regulatory complaints. And experienced 70% fewer public relations channels related to AI planets. So a robust ethical framework seamlessly integrates technical safeguards with comprehensive governance structure to ensure responsibly AI deployment throughout the entire lifecycle. So organizations that strategically prioritize ethical considerations, not only mitigate operational under. Reputational risks, but circulate to it, but also cultivate enduring trust with customers, partners, regulators, and product case study, the financial services AI cloud transformation with challenge. So entire global bank struggled with a 43% increase in sophisticated fraud attacks. Awis average customer response, strength on demand regulatory, their fragmented legacy and vouchers could not support advanced AMLs without exposing critical vulnerabilities in data production. So implementation. So the bank are, uh, zero. Trust A cloud platform incorporated. It's one 40 MOREOR encryption for real time data analysis without decryption, so it still service for service. Secure microservices, orchestration and and ethical a governance framework with automated data objection across 37 demographic variables, which results fraud detection accuracy improved by 18% while reducing false false tools by 60%. Customer response times drop from to two minutes and page 19.7 bucks. So the bank actually a validated 2 87 within three years while maintaining technic a transparency scores about industry benchmarks. So this transformation creates how financial institutions can effectively navigate the complex challenges of. AI Cloud integrations while simultaneously addressing security vulnerabilities, operational inefficiencies and ethical considerations. So by implementing a comprehensive technical and governance approach, the bank not only delivered quantifiable performance implements, but also strengthen the customer trust, established a and established a leadership position in responsible AI deployment. Future trends in integration. So quantum AI computing, so quantum computing will fundamentally transform AI capabilities by tracking complex problems on unprecedented speeds by the name quantum enhanced emission planning algorithm have already demonstrated 1 94 x performance improvements ion task critical to financial modeling and pharmaceutical research. Edge Cloud hybrid models. A seamless integration between edge and cloud AI process is creating distributed intelligence networks without additional borders. So organizations implementing Edge Cloud hybrid architecture experience 6% latency and power conversion, augmenting centralized management capabilities. The next self-improving a s systems. So the emergence of AA systems capable of autonomously enhancing data algorithms and that are shipped in Comput, early prototypes have proven their ability to optimize cloud resource location of percent percent more effectively than export human engineers. So these transformative trends will reshape the AI cloud ecosystems. Unlocking unprecedented opportunities while introducing sophisticated challenges in governance, security, and ethical implementation. So organizations that strategically prepare for these development will gain competitive advantages in the next. Digital transformation. The final takeaway Successfully integration with cloud competing demands. A strategic three tire approach addressing critical security, operational, and critical dimensions. So automations must for established a robust security rob, robust security foundation, which advance encryption and federated. Build operational excellence through modern architectural practices, and finally implement rigorous ethical governance to ensure responsible AI deployment. So this comprehensive pyramid approach enables Orions to maximize the transformative benefits of AI cloud synergy driving operational efficiency, enhancing data driven mission making, and creating sustainable competitive advantages. So while effectively. Mitigating risks and cultivated last trust among customers, regulators, and stakeholders. So Ali, thank you. Thank you all.