Latest news with #agenticAI
Forbes
7 hours ago
- Forbes
The Rise Of Autonomous Cyber Agents
Ronen Cojocaru, Co-CEO and Co-founder, Imperative Inc. getty Artificial intelligence is rapidly evolving from passive tools into autonomous "agentic" systems capable of making decisions and taking actions without direct human input. These AI agents are already proving valuable as co-pilots to human analysts, enhancing threat detection and speeding up incident response. Yet their growing autonomy is a double-edged sword. As these agents gain more power, ensuring they remain secure, transparent and reliable becomes paramount. Early examples of agentic AI in cybersecurity, from automated threat-hunting bots to self-driving network monitors, demonstrate huge potential. However, they also highlight new vulnerabilities. AI agents can, unfortunately, be easily tricked or influenced by bad data, sometimes resorting to biased or incorrect assumptions, and users may place misplaced confidence in their outputs. In short, agentic AI is a force multiplier for cyber defense, but without proper safeguards, it can just as easily multiply cyber risk. Despite the promise, security leaders must grapple with several emerging risks from agentic AI systems. Notably, model drift, malicious manipulation and operational reliability issues are front and center: Model Drift Over time, AI models can become misaligned with reality as their input data changes—a phenomenon known as 'data drift.' This natural degradation in data characteristics means an AI that once performed well might start making errors as its environment evolves. For example, an intrusion detection model trained on last year's network traffic may gradually falter as new apps, devices and attacker techniques appear. Such drift opens up new attack surfaces if not caught and corrected, undermining the model's effectiveness. Recognizing this, recent joint security guidance from the U.S. and allies urges companies to monitor AI performance closely and treat drift as an expected challenge. Agentic AIs are vulnerable to adversarial exploits. Hackers can attempt to manipulate an AI's inputs or training data to distort its behavior. Tactics like data poisoning and feeding incorrect or malicious data into an AI's training pipeline can wreak havoc on its decision making. Imagine an attacker subtly corrupting the data that trains a spam filter or fraud detector—the AI might then start letting threats slip through or flagging the wrong items. Officials worldwide are increasingly fearful of hackers manipulating AI systems, especially those deployed in critical infrastructure. A poisoned or manipulated model not only makes bad choices; it erodes confidence that AI outputs can be trusted at all. Operational Reliability And Trust Like all AI, autonomous agents suffer from issues of hallucination, bias and erratic behavior, which can be amplified by their autonomy. Without proper governance, an AI agent might confidently produce incorrect analyses or take unauthorized steps. These problems aren't just theoretical—early deployments have shown that AI assistants can 'go rogue' or output toxic content if misused. Businesses have learned that an unsupervised agent's mistake can lead to serious harm, reputational damage or compliance violations. Moreover, when AI agents act unpredictably, humans tend to either over-trust them or distrust them entirely—both scenarios are risky. As one expert noted, current AI agents are still 'easily tricked' and prone to biased assumptions, yet people often trust their answers when they shouldn't. Ensuring reliability means building in rigorous testing, guardrails and oversight for AI decisions. In practice, companies are putting 'human in the loop' controls on critical uses and instituting AI red-team exercises to probe for failure modes. The goal is an AI that operates responsibly and transparently, earning trust through consistent and correct performance. Future Outlook: Roadmap For AI-Powered Cybersecurity While today's agentic AI is still maturing, the coming years promise a dramatic expansion of AI's role in cybersecurity. In this phase, organizations move from experimentation to real deployments of agentic AI for security. AI co-pilots become common in security operations centers, handling routine tasks and assisting human analysts. For instance, autonomous AI agents might triage alerts, scour logs for threats or automate responses to basic incidents. These early agentic systems are generally narrow in scope and operate under human supervision, reflecting lessons learned about governance. Shadow AI agents (unsanctioned bots running without oversight) emerge as a concern, prompting companies to institute AI governance programs. Industry experts emphasize the need for visibility into all AI agents in use and strict alignment with security policies to avoid 'rogue' deployments. Notably, businesses begin to treat AI agents much like employees: vetting their 'credentials,' monitoring their activities and granting only least-privilege access. As one analysis put it, AI agents can indeed augment overworked cyber teams, but only if we ensure these agents are deployed in a secure, explainable and reliable manner. Looking a bit further out, 2026 is expected to usher in swarm intelligence and collective defense enabled by networks of AI agents. Rather than working in isolation, multiple AI systems will increasingly communicate, collaborate and even negotiate with each other across networks. Cyber defenses could be handled by fleets of specialized AI agents, with one set watching network traffic, another analyzing user behaviors and others managing endpoint security—all sharing intelligence in real time. This coordinated 'swarm' of AI agents can respond to threats faster than any single system, mimicking a colony of ants or bees that collectively defend their nest. A new challenge will be understanding the emergent behavior of interacting AIs. When dozens of semi-autonomous agents interconnect, unexpected dynamics may arise not unlike complex financial markets or ecosystems. By the late 2020s, the industry anticipates a transition from narrow AI tools to cognitive cybersecurity ecosystems. In practice, this means AI systems with advanced reasoning capabilities are deeply integrated into every facet of cyber defense. For example, cyber defense systems will leverage AI that emulates human-like thinking and learning processes. These cognitive SOCs can ingest vast, diverse data streams, network logs, threat intel feeds, user activity and more to make connections that human analysts might miss. Cybersecurity ecosystems will become adaptive and self-optimizing. AI will not just react to attacks but continuously learn from them, evolving its defenses. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
a day ago
- Business
- Yahoo
AI in cards and payments: Revolutionising the future of transactions
Artificial Intelligence (AI) is transforming the cards and payments industry, making transactions faster, safer, and more personalised. As digital wallets, contactless payments, and real-time transaction systems grow globally, AI is becoming a core driver of innovation. Today, this evolution is entering a new phase with the rise of agentic AI—intelligent systems capable of autonomous, goal-directed action that can make decisions, learn from outcomes, and adapt without human input. Fraud prevention has always been a critical concern for payment providers. Traditional systems rely on pre-set rules, which can miss new types of fraud or trigger unnecessary alerts. AI, especially in its agentic form, is more adaptive. It not only identifies unusual patterns but can act in real time—flagging suspicious activity, freezing compromised accounts, and triggering additional authentication—all without waiting for manual reviews. Agentic AI systems can also collaborate across networks, detecting coordinated fraud across multiple platforms. These systems continuously learn, update their models instantly, and improve detection accuracy while reducing false positives, ensuring legitimate transactions proceed smoothly. Modern consumers expect personalised, intuitive financial services. AI makes this possible by analysing spending habits, preferences, and even life events to offer relevant product suggestions or spending insights. But agentic AI takes this further. Instead of just offering recommendations, these AI agents can act on behalf of users. For example, they might automatically choose the best card for a transaction to maximise rewards, shift funds between accounts to avoid overdrafts, or suggest and initiate a savings plan based on monthly spending. These agents don't just react—they proactively manage financial tasks in line with user goals and preferences. Real-time payment systems are now essential, with users expecting instant, error-free transactions. Agentic AI helps make this possible by optimising payment routing, predicting network congestion, and ensuring liquidity across accounts—all in the background. Such AI systems can autonomously select the best path for a payment based on speed, cost, and reliability. In complex environments like cross-border transactions, agentic AI manages currency conversions, compliance requirements, and settlement risks without human intervention, improving efficiency and transparency. AI chatbots have already enhanced customer service by providing instant support for common queries. However, with agentic AI, these assistants evolve into financial agents—capable of taking meaningful actions rather than just responding to questions. Imagine telling your assistant, 'I'm traveling to Spain tomorrow,' and it automatically enables international payments, suggests travel insurance, checks for foreign transaction fees, and informs your bank's fraud system. This level of autonomous service radically enhances convenience and customer engagement. Payments processing involves numerous behind-the-scenes workflows—from reconciliation and compliance to chargeback handling. AI already automates many of these tasks, but agentic AI can go further by managing them. These systems can detect inefficiencies, redesign workflows, and implement improvements independently. For example, if recurring issues arise with transaction failures, an AI agent can analyse root causes, adjust protocols, and even communicate updates to support teams—streamlining operations and cutting costs. As regulatory expectations grow, financial institutions must ensure not just compliance, but explainability. Agentic AI offers built-in auditing capabilities, tracking every decision and offering justifications in human-readable formats. This helps meet regulations like PSD2 and GDPR while building trust with users and regulators alike. The cards and payments industry is not just digitising—it's becoming autonomous. With the rise of agentic AI, systems now go beyond prediction to decision-making, self-management, and proactive service delivery. From fraud prevention and customer experience to risk management and compliance, AI is redefining every aspect of how value moves. As we move forward, the financial institutions that embrace agentic AI won't just offer better services—they'll pioneer the next era of intelligent, self-directed finance. The future of payments is not just smart—it's agentic. And it's already here. Vivek Dwivedi is Regional Head - Cards and Payments, Financial Services at Infosys "AI in cards and payments: Revolutionising the future of transactions" was originally created and published by Electronic Payments International, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.
Arab News
2 days ago
- Business
- Arab News
Deloitte Middle East advances AI integration with launch of Global Agentic Network
Deloitte has launched its Global Agentic Network, a strategic initiative designed to scale AI-driven digital workforce solutions for organizations around the world, with significant potential to transform business operations across the Middle East. As AI adoption accelerates in the region, Deloitte's agentic AI offering provides a future-forward solution that combines intelligent automation with human expertise. Through its global network spanning EMEA, Asia Pacific, and North America — and with a growing regional focus in the GCC — Deloitte is bringing AI-powered agents to enterprises looking to drive operational efficiency, accelerate growth, and reimagine how work gets done. Agentic AI refers to software agents capable of autonomously executing tasks, orchestrating workflows, and adapting based on input from users or other systems. These agents, powered by large language models and machine learning, are designed to learn and evolve — making them ideal for complex, dynamic business environments. In the Middle East, where government and private sector agendas alike are emphasizing digital transformation, the Global Agentic Network supports national strategies for AI innovation and economic diversification. Deloitte is already supporting regional clients in sectors such as energy, government, and financial services to implement agentic solutions that streamline decision-making, improve efficiency, and unlock value at scale. 'The Middle East is on a rapid trajectory toward AI-led transformation, and Agentic AI is a game-changer for how businesses operate,' said Yousef Barkawie, Deloitte Middle East Gen AI leader. 'At Deloitte, we're helping our clients navigate the world of AI transformation by architecting and building the capabilities and trust needed for them to scale out their AI deployments and transform at the core. Our clients are finding new efficiencies in their ways of working, streamlining their operations, and reimagining their entire value chains. This is an exciting moment to help shape what the future of work looks like in our region, especially as governments and industries double down on innovation and future-readiness.' The Global Agentic Network includes alliances with leading technology platforms and the launch of solutions like Zora AI, Deloitte's suite of proprietary AI agents that can autonomously perform complex business functions. These tools are already being deployed within Deloitte's own operations, as part of the firm's broader ambition to become an AI-fueled organization by 2030.
Globe and Mail
2 days ago
- Business
- Globe and Mail
IBM Introduces Industry-First Software to Unify Agentic Governance and Security
New IBM integrations to help businesses keep their agentic AI – and other generative AI systems –secured and responsible at scale Enterprises can red team agents, audit agents, detect shadow agents, and more
Yahoo
3 days ago
- Business
- Yahoo
Agentic AI integration set to accelerate this year among Gen AI early adopters
Press contact: Antara NandyTel.: +91 9674515119 Email: Agentic AI integration set to accelerate this year among Gen AI early adopters Two in five organizations expect to achieve positive return on their AI investments in 1-3 years By embedding a targeted set of AI capabilities into core business processes such as procurement, customer service, supply chain optimization, and finance operations, organizations are already achieving significant cost efficiencies Paris, June 18, 2025 – A Capgemini Research Institute report published today, finds that AI is now driving positive returns on investment (ROI), with the average being nearly a 1.7 times return. The report highlights that this has now laid the groundwork for widespread agentic AI implementation. Among those early adopter organizations that have implemented generative AI (Gen AI), around 30% have already integrated AI agents into their business operations. Agentic AI projects are expected to rise by 48% by the end of 2025. The research also finds that one in five organizations already use AI agents or multi-agent systems, with Gen AI and agentic AI already delivering significant cost savings and operational efficiencies in business functions. With businesses planning investments in AI infrastructure, some organizations had expressed concerns about achieving ROI from their large-scale AI and Gen AI rollouts. However, the report finds that these initial concerns are fading fast, as enterprises are now seeing substantial returns, with those surveyed achieving a 1.7 times ROI from their Gen AI and AI investments. As a result, enterprises are increasing their Gen AI investments, with 62% of those surveyed growing their investment in Gen AI this year as compared to last year. 'Gen AI and agentic AI can truly transform business services – enabling the shift from traditional cost-focused models towards an AI-enabled, value and insight driven business. Those that adopt an integrated approach with data and AI at its core will be set to achieve a truly connected, frictionless enterprise,' said Oliver Pfeil, CEO of Business Services at Capgemini and Member of the Group Executive Committee. 'While the research suggests increased adoption of AI agents, organizations still face numerous barriers to implementation at scale. Adopting a pragmatic approach, fostering trust in AI, and creating a strong data foundation will go a long way in transforming business services into a strategic powerhouse to fuel any enterprise.' Gen AI adoption has laid the groundwork for agentic AI implementationGen AI is expected to drive improvements in key metrics such as insight accuracy, productivity, time to market, and customer and employee experience over the next three years. As a result, more businesses are seeing the value of Gen AI, with 36% of organizations already implementing it, up from 20% last year. Among those that have adopted Gen AI at a limited or full scale, around 30% have integrated AI agents into their operations. The total number of AI agent projects in an average organization are expected to grow 48% in 2025. According to the report, AI agents are already delivering significant benefits across business functions, with agents and multi-agent systems reducing errors, improving customer satisfaction levels, increasing operational efficiency, and reducing operational costs. The top five industries adopting AI agents are high tech, industrial manufacturing, consumer products, energy & utilities, and pharma & healthcare. Strong leadership and workforce transformation are key to faster returnsTo achieve strong ROI on Gen AI investments, organizations should focus on developing strong leadership, governance, and AI readiness. According to the report, organizations who establish this foundation achieve ROI 45% faster. However, most enterprises currently lack this strong leadership, with only one in three leaders being a strong advocate of Gen AI. In addition, organizations must also transform their workforce to derive business value cites the report. In the past two years, enterprises that introduced automation and AI-based use cases have been able to automate 30% of operational tasks, and expect to automate further in the next two years. As responsibilities evolve, organizational upskilling, reskilling, training and job role transitions will feature highly, with almost two-thirds of employees expecting to see their job descriptions altered by 2028. According to the report, employee interaction with AI agents is expected to increase by 2028, so training and upskilling will be needed to prepare workforces for effective human-AI collaboration. Report MethodologyThe Capgemini Research Institute conducted a survey of 1,607 executives from organizations with at least $1 billion in global revenue in the last financial year, who are responsible and accountable for one or more AI and gen AI initiatives in business operations. Executives were from supply chain & procurement, finance & accounting, people operations, customer operations, AI leadership and strategy, AI application development and maintenance, AI ethics, regulations, and compliance functions. The executives were from 15 countries across multiple regions and spanning 13 industries. The Institute also interviewed 15 senior executives leading business operations and AI implementation at their respective organizations from across sectors and countries. About CapgeminiCapgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2024 global revenues of €22.1 billion. Get The Future You Want | About the Capgemini Research InstituteThe Capgemini Research Institute is Capgemini's in-house think-tank on all things digital. The Institute publishes research on the impact of digital technologies on large traditional businesses. The team draws on the worldwide network of Capgemini experts and works closely with academic and technology partners. The Institute has dedicated research centers in India, Singapore, the United Kingdom and the United States. It was ranked #1 in the world for the quality of its research by independent analysts for six consecutive times - an industry first. Visit us at Attachments 06_18_Capgemini news alert_AI in Business Operations CRI report Final-Infographic-AI-in-Business-Operations
