AI in banking to dominate talks at IBAs 2025 event with Wegofin leading the way Zee Business

Is cloud-based AI becoming a monopoly?

Looking ahead, the prospects for generative AI in cybersecurity are promising, with ongoing advancements expected to further enhance threat detection capabilities and automate security operations. Companies and security firms worldwide are investing in this technology to streamline security protocols, improve response times, and bolster their defenses against emerging threats. As the field continues to evolve, it will be crucial to balance the transformative potential of generative AI with appropriate oversight and regulation to mitigate risks and maximize its benefits [7][8].

According to McKinsey, a staggering 65% of organizations are actively integrating GenAI, nearly doubling its adoption within a year. From cybersecurity to healthcare, manufacturing to banking, AI’s all-pervasive influence marked an era of rapid adoption and strategic integration into workflows, decision-making, and customer interactions. From healthcare to manufacturing, AI has emerged as a transformative force, unlocking efficiency, redefining workflows, and setting new benchmarks for innovation across sectors. The gathering is set to attract over 350 brightest minds, from a diverse mix of bankers, and regulatory officials, fostering an intellectual dialogue on the role of fintech enablers technology in driving India’s financial future. By bringing together these thought leaders and innovators, IBA continues to catalyze the adoption of next-generation solutions, setting benchmarks for the industry.

AI in banking to dominate talks at IBA’s 2025 event with Wegofin leading the way

Security teams can detect and analyze potential vulnerabilities in real-time by monitoring network traffic and API interactions. With centralized policy enforcement via Cisco’s Security Cloud Control, SecOps teams can manage security across multiple AI applications and enforcement points from a single interface to reduce complexity and operational overhead. The traditional approach of hands-on management is gradually shifting towards a more supervisory role where project managers oversee AI-driven processes and ensure their alignment with project goals [3]. This shift necessitates a deeper understanding of AI technologies and their applications in project management [4]. Furthermore, as GenAI systems become more advanced, project managers may find themselves increasingly involved in AI training and customization to ensure these systems align with their specific project needs [8]. Generative AI (GenAI) is revolutionizing the field of project management by automating numerous routine tasks, thus enabling project managers to concentrate on strategic aspects and overall project output.

Predictive analytics remained indispensable for businesses seeking to optimize operations and make informed decisions. One of the biggest highlights of this prestigious event is the participation of Wegofin, a leading fintech innovator, as a key sponsor, which is a testament to its commitment to advancing the fintech landscape. In an era of technological sophistication, it is vital to maintain an environment that fosters competition. Some may predict a future dominated by a few tech giants, but the landscape of AI is too vibrant and expansive to be limited by just a handful of companies. Someday, I may regret writing this article, but for now, this is my story, and I’m sticking to it. On the other hand, we’re still figuring out how to make sense of data privacy, inclusivity, and equitable access—like someone buying a top-of-the-line drone but forgetting to read the instruction manual.

Cisco AI Defense helps developers protect AI systems from attacks and safeguards model behavior across platforms. Security teams must understand who is building applications and the training sources for these new applications. Cisco AI Defense provides security teams with visibility into all third-party AI applications used within an organization, including tools for conversational chat, code assistance, and image editing. Dave has authored 13 books on computing, the latest of which is An Insider’s Guide to Cloud Computing. Dave’s industry experience includes tenures as CTO and CEO of several successful software companies, and upper-level management positions in Fortune 100 companies.

This integration ensures that all data-driven decisions are based on the same accurate and up-to-date information, enhancing overall operational efficiency. Addressing these challenges requires proactive measures, including AI ethics reviews and robust data governance policies[12]. Collaboration between technologists, legal experts, and policymakers is essential to develop effective legal and ethical frameworks that can keep pace with the rapid advancements in AI technology[12]. Generative AI technologies utilizing natural language processing (NLP) allow analysts to ask complex questions regarding threats and adversary behavior, returning rapid and accurate responses[4]. These AI models, such as those hosted on platforms like Google Cloud AI, provide natural language summaries and insights, offering recommended actions against detected threats[4]. This capability is critical, given the sophisticated nature of threats posed by malicious actors who use AI with increasing speed and scale[4].

Additionally, GenAI assists in risk management by analyzing data to identify potential risks and generate insights for proactive decision-making[4]. The future of generative AI in combating cybersecurity threats looks promising due to its potential to revolutionize threat detection and response mechanisms. This technology not only aids in identifying and neutralizing cyber threats more efficiently but also automates routine security tasks, allowing cybersecurity professionals to concentrate on more complex challenges [3]. Instead of treating security as an add-on, service providers are integrating cybersecurity measures directly into their network offerings.

This transformation enables service providers to offer high security postures while maintaining performance. By integrating cybersecurity at the network level, providers can mitigate threats without compromising speed or reliability. Enterprises benefit from seamless protection against malware, exploits, and evasions ensuring data integrity and user trust. This approach not only enhances resilience against emerging cyber threats but also instills confidence among enterprises navigating an increasingly complex threat landscape. Automation will empower service providers to deliver a cloud-like experience for network services, supporting on-demand connectivity, multi-cloud integration, and superior application performance.

The application of generative AI in cybersecurity is further complicated by issues of bias and discrimination, as the models are trained on datasets that may perpetuate existing prejudices. This raises concerns about the fairness and impartiality of AI-generated outputs, particularly in security contexts where accuracy is critical. Countries are crafting national AI strategies to ensure global competitiveness and address issues like governance and trust. In India, the government’s focus on AI-ready infrastructure, research and development, and skilling programs has accelerated the adoption of advanced AI models. Vyas emphasizes, “Boards are increasingly focused on integrating sustainability into their core operations. A McKinsey report supports this, showing that 81% of consumers are more likely to engage with companies they trust with their data.

We also expect him to emphasize how Gen AI would continue to contribute to enhancing decision-making capabilities, fortifying security frameworks, and reducing friction in transactions. Issues related to the quality of results, potential misuse, and the disruption of existing business models are significant concerns[2]. Moreover, GenAI can sometimes provide inaccurate or misleading information, which requires vigilant oversight and validation by project managers[2]. To address these concerns, technologies that ensure AI trust and transparency are becoming increasingly important[4]. Moreover, generative AI technologies can be exploited by cybercriminals to create sophisticated threats, such as malware and phishing scams, at an unprecedented scale[4]. The same capabilities that enhance threat detection can be reversed by adversaries to identify and exploit vulnerabilities in security systems [3].

Generative AI Technologies

To function as a true as-a-service offering, networks require end-to-end automation, including supply chain processes, API integration, and smart contracts. This level of automation ensures real-time responses and seamless connectivity, enabling users to provision and scale network resources instantly. The rise of generative AI and agentic AI, which emphasizes reasoning and autonomy, is set to transform applications across industries.

These AI systems rely on a mix of foundational and specialized models to deliver results, necessitating networks that are both highly reliable and predictable. As generative and agentic AI revolutionize industries, telecom networks must deliver unparalleled reliability and low latency. Meanwhile, NaaS continues to redefine network delivery through automation and innovative commercial models.

By clicking the button, I accept the Terms of Use of the service and its Privacy Policy, as well as consent to the processing of personal data. He’s no stranger in this area having covered mobile phones and gadgets since 2008 when he started his career. On top of his editor duties, he’s a seasoned videographer being in front and behind the camera producing YouTube videos. Outside of tech, he enjoys producing mini documentaries and fun social clips for small businesses, enjoying the beach life at the Jersey Shore, and recently becoming a first time homeowner.

This efficiency allows organizations to detect threats with the same speed and sophistication as the attackers, ultimately enhancing their security posture[4]. Generative AI has emerged as a pivotal tool in enhancing cyber security strategies, enabling more efficient and proactive threat detection and response mechanisms. As the shortage of advanced security personnel becomes a global issue, the use of generative AI in security operations is becoming essential. For instance, generative AI aids in the automatic generation of investigation queries during threat hunting and reduces false positives in security incident detection, thereby assisting security operations center (SOC) analysts[2].

The adoption of GenAI in project management accelerates processes by streamlining routine operations, freeing teams to focus on high-value work[3]. For example, in the manufacturing, automotive, aerospace, and defense industries, generative design can optimize designs to meet specific goals and constraints, such as performance and materials, thereby speeding up the design process[4]. The integration of federated deep learning in cybersecurity offers improved security and privacy measures by detecting cybersecurity attacks and reducing data leakage risks. Combining federated learning with blockchain technology further reinforces security control over stored and shared data in IoT networks[8]. MEF continues to play a pivotal role in driving industry standards and certifications, fostering collaboration, and enabling innovation to help expand the connectivity and service delivery ecosystem.

Over half of executives believe that generative AI aids in better allocation of resources, capacity, talent, or skills, which is essential for maintaining robust cybersecurity operations[4]. Despite its powerful capabilities, it’s crucial to employ generative AI to augment, rather than replace, human oversight, ensuring that its deployment aligns with ethical standards and company values [5]. As it continuously learns from data, it evolves to meet new threats, ensuring that detection mechanisms stay ahead of potential attackers [3]. This proactive approach significantly reduces the risk of breaches and minimizes the impact of those that do occur, providing detailed insights into threat vectors and attack strategies [3]. ANNs are widely used machine learning methods that have been particularly effective in detecting malware and other cybersecurity threats.

Such applications underscore the transformative potential of generative AI in modern cyber defense strategies, providing both new challenges and opportunities for security professionals to address the evolving threat landscape. In a novel approach to cyber threat-hunting, the combination of generative adversarial networks and Transformer-based models is used to identify and avert attacks in real time. This methodology is particularly effective in intrusion detection systems (IDS), especially in the rapidly growing IoT landscape, where efficient mitigation of cyber threats is crucial[8]. By enabling efficient bandwidth, data routing and minimizing latency, networks will support the rapid growth of AI applications, fostering innovation across sectors. AI’s expansion into areas like healthcare diagnostics, financial modeling, autonomous systems, and other critical industries, underscores the urgency for telecom providers to stay ahead.

• Generative AI (GenAI) has significantly impacted Agile and Scaled Agile Framework (SAFe) practices by enhancing flexibility, efficiency, and responsiveness within project management workflows.
• I’m not sure that ever helps except in exceptionally dire circumstances, such as breaking up Ma Bell in the 1980s.
• The rise of cloud computing and AI has been exponential and will continue to thrive, even when cloud-based AI systems are significantly more expensive than private servers.

This evolution of NaaS will also unlock new commercial models, fostering innovation and simplifying network consumption for enterprises worldwide. Enterprises will benefit from enhanced operational efficiency, observability, the ability to scale resources dynamically, and simplified interactions with service providers. By embracing NaaS, businesses can focus on growth and innovation while leaving the complexities of network management to automated systems. One of the key impacts of GenAI in project management is its ability to intelligently assign tasks, predict potential bottlenecks, and suggest optimal workflows. This makes project planning more dynamic and responsive, allowing project managers to import their current workflows into tools like Dart AI to utilize features such as intelligent planning[5]. For instance, Dart AI can deconstruct a complex project, create a roadmap, and help determine a realistic timeframe for completion[5].

Why does Cisco AI Defense matter?

Businesses must now prioritize privacy, security, and actionable control over sprawling data ecosystems. This shift has broken down traditional silos between IT teams and data owners, fostering a more collaborative and data-centric approach. Self-service tools like Zubin are empowering users to manage sprawling data ecosystems with minimal IT intervention.

No-code and low-code platforms symbolize a future where AI is not just for the privileged few but a tool for anyone with a vision. The challenge ahead is to harness this power responsibly, ensuring that innovation serves as an equalizer rather than a disruptor. Businesses can now deploy AI models in days rather than months, saving costs and reducing dependency on technical experts. Over-simplification of AI models can lead to inaccurate or biased results, while data privacy concerns loom large due to the cloud-based nature of many platforms.

For industries like healthcare and BFSI, these tools transform governance into an intuitive process, reducing data sprawl and unlocking innovation. The year’s most transformative trend has undoubtedly been the widespread adoption of Generative AI, a subset of artificial intelligence that moves beyond prediction to creation. It has enabled businesses to not only solve problems but anticipate needs and proactively address them. Yes, the emerging companies are disruptors, a word I hate using to describe technology and tech companies.

He keynotes leading technology conferences on cloud computing, SOA, enterprise application integration, and enterprise architecture. Very few AI systems are built these days that do not involve Microsoft, Google, or AWS’s cloud services. The FTC highlighted how these partnerships enable Big Cloud to extract significant concessions from developers. This may lock users into ecosystems that favor big players and sideline smaller, innovative companies that could drive AI advancements. Generative AI (GenAI) has significantly impacted Agile and Scaled Agile Framework (SAFe) practices by enhancing flexibility, efficiency, and responsiveness within project management workflows. Agile and SAFe methodologies emphasize iterative progress, collaboration, and continuous feedback, which are well-supported by the capabilities of GenAI.

” A few key players dominate the landscape, but competitive tension has historically driven technology forward. Indeed, the CMA’s recent assessment of Alphabet and Anthropic determined that the partnerships did not constitute a merger that would significantly impair competition. This not only indicates a comprehensive understanding of the tech landscape but also supports the notion that opportunities for competition exist despite the presence of large partnerships. Scrutiny encourages compliance and inspires organizations to explore novel ideas and alternatives to stand out in the market. It’s heartening, of course, to see policymakers draft ambitious blueprints, albeit with the occasional “fine print” that makes you wonder if they consulted a data scientist or just a lawyer with a thesaurus.

Advanced network capabilities will support AI’s technical requirements and empower industries to unlock new efficiencies and transformative opportunities. Over the past several years, the security landscape rapidly evolved with the introduction of AI, specifically generative AI. AI spawned numerous new categories of AI cyber threats, such as data inference, transfer learning attacks and model inversion. Today, companies need specialized security solutions that protect AI systems and their components from various security threats (e.g., adversarial attacks) and vulnerabilities (e.g., data poisoning).

Companies like IBM are already investing in this technology, with plans to release generative AI security capabilities that automate manual tasks, optimize security teams’ time, and improve overall performance and effectiveness[4]. These advancements include creating simple summaries of security incidents, enhancing threat intelligence capabilities, and automatically responding to security threats[4]. Another case study focuses on the integration of generative AI into cybersecurity frameworks to improve the identification and prevention of cyber intrusions.

Smaller firms and independent developers often take market leaders’ cues yet build solutions catering to niche needs, further enriching the AI marketplace. The big guys have their thumbs in that pie as well, and their developers also make significant contributions; a $500k investment is almost commonplace these days. Large Language Models (LLMs), a subset of GenAI, facilitate multilingual support by translating queries and responses in real time. This capability ensures effective communication and collaboration among diverse, global teams, which is increasingly common in Agile and SAFe practices[10]. The real-time translation aids in eliminating language barriers, thereby fostering a more inclusive and efficient working environment. For instance, GenAI most commonly creates content in response to natural language requests and doesn’t require knowledge of or entering code, making it accessible to a broader range of users[4].

The concept of utilizing artificial intelligence in cybersecurity has evolved significantly over the years. One of the earliest types of neural networks, the perceptron, was created by Frank Rosenblatt in 1958, setting the stage for the development of more advanced AI systems like feedforward neural networks or multi-layer perceptrons (MLPs)[1]. Generative AI, particularly models such as ChatGPT that use large-scale language models (LLM), has introduced a new dimension to cybersecurity due to its high degree of versatility and potential impact across the cybersecurity field[2]. This technology has brought both opportunities and challenges, as it enhances the ability to detect and neutralize cyber threats while also posing risks if exploited by cybercriminals [3]. The dual nature of generative AI in cybersecurity underscores the need for careful implementation and regulation to harness its benefits while mitigating potential drawbacks[4] [5].

Additionally, tools like Dart AI can break down complex projects, plot them on a roadmap, and help determine realistic timelines for project completion[5]. Generative AI (GenAI) and machine learning (ML) are both integral components of artificial intelligence, yet they serve different purposes and functionalities. GenAI is a form of AI/ML technology that aims to make accurate predictions about what users want and then provide new content accordingly[1]. This involves extensive machine learning model training and massive data sets, allowing GenAI tools to generate novel content such as text, images, and more, based on patterns and inputs received from users[1].

In contrast, ML often involves more technical expertise and a deeper understanding of data science principles to develop and deploy models effectively. While ML provides insights and predictions based on data analysis, GenAI creates new, original content that can be used in various innovative ways[3]. One prominent example is ChatGPT, a GenAI tool that generates human-like text based on user prompts.