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Forbes
02-05-2025
- Business
- Forbes
Outpacing Security: A Cyber Resilience Mandate For Modern Leaders
High quality 3D rendered image, perfectly usable for topics related to big data, global networks, ... More international flight routes or the spread of a pandemic / computer virus. Textures courtesy of NASA: Fraud is as old as commerce itself. From Ponzi schemes to phishing scams, bad actors have always exploited moments of technological or systemic transition. But with the rapid emergence of artificial intelligence, we've entered a new era—one where the tools used to enhance performance, automate workflows, and elevate customer experience are also being weaponized in real time. At EXCELR8, we've work closely with global banks and enterprise institutions for years - often with their digital and IT divisions. What we're seeing firsthand is that AI's transformative promise is accompanied by an equally urgent need for robust, adaptive cybersecurity strategies. In other words, when the way we work and interact changes this fast—so too do the threats. According to Team8, AI-powered impersonation attacks increased by over 300% year-over-year—making financial scams the fastest-growing category of fraud in banking. What was once limited to clever emails and forged documents is now a world of deepfakes, synthetic identities, voice clones, and hyper-personalized scam attempts that can be deployed at scale. In efforts to dig deeper, I reached out to some experts. As fellow tech founder and entrepreneur Roy Zur - CEO of Charm Security - puts it, 'Generative AI has dramatically amplified scam risks for financial institutions by enabling highly convincing scams at scale. Financial losses are only part of the story—it's also about reputational damage, trust erosion, and regulatory exposure.' For many of today's leadership teams - depending upon the industry, fraud prevention can no longer be confined to the domain of cybersecurity or compliance alone—it has become a core business imperative, demanding organization-wide alignment. As generative AI accelerates both the sophistication and scale of fraudulent activity, executive accountability has expanded accordingly. Encouragingly, 84% of senior bank executives and board members now identify scams and cyber-enabled fraud as top-tier threats, according to Bank Director's 2025 Risk Survey. Yet despite this heightened awareness, most institutions still lack a comprehensive, cross-functional strategy—particularly one that addresses the human element of fraud: behavioral vulnerability, organizational blind spots, and the cultural norms that shape how risk is recognized and escalated. Financial institutions are leading the way in this area. Banks like JPMorgan Chase, which suffered $500 million in losses due to fraud last year, are now using AI models that continuously learn from new data to detect and block suspicious activity, resulting in lower levels of fraud and a better customer experience, with account validation rejection rates cut by 15-20 percent. Ironically, the very technologies that introduce new risks also represent our best defense. From machine learning that monitors behavioral anomalies to generative AI agents that model fraud scenarios before they occur, cybersecurity is evolving into a predictive discipline. Global banks are now deploying AI tools to: Even more advanced institutions are running AI-powered red-team (a process we used in the Navy SEAL teams to poke holes on our mission plans) simulations to proactively train fraud teams in real-time scam defense—ensuring organizations are prepared for evolving threat vectors before they materialize. At EXCELR8, we've observed how these security advancements must be embedded not just into tech stacks, but also into leadership thinking, culture, and execution platforms. Gone are the days when fraud prevention was relegated to a risk department or IT function. In today's landscape, cybersecurity must be a cross-functional priority that spans operations, leadership, product, and workforce enablement. What we're seeing in our work with enterprise organizations is a shift toward shared ownership models: This is where organizations often fall short—not in tools, but in coordination. And that's what separates the reactive from the resilient. Despite advancements in AI-driven security, human error remains one of the most vulnerable entry points. Social engineering, phishing, and manipulation still succeed because systems are fallible—but so are people. Leading institutions are changing that. We've seen them go beyond standard compliance training and implement: Cybersecurity awareness is becoming less of a task and more of a team trait. It's about creating a workforce that doesn't just follow security protocols—but thinks in terms of security instinctively. The next era of organizational growth will not be shaped solely by AI innovation—but by how confidently and securely that innovation is deployed. It's about building environments where velocity and vigilance coexist. Enterprise AI must be built on trustworthy, transparent systems that support secure collaboration, role-specific visibility, and AI-driven decision support—without ever compromising data privacy, regulatory integrity, or user autonomy. In our work with financial institutions, we've seen that the most successful leaders don't just prepare for cyber threats—they engineer resilience into the organization itself. They combine advanced technology with governance. They pair performance acceleration with risk mitigation. They understand that if speed is the strategy, security is the foundation. AI will continue to redefine how we operate, compete, and collaborate. But it's not neutral. It amplifies whatever systems, behaviors, and risks we've already built. Which is why every conversation about innovation must be matched by a conversation about integrity, security, and trust. Because in the age of intelligent systems, fraud prevention isn't just a function—it's a leadership mandate.
Forbes
28-04-2025
- Business
- Forbes
How Business Leaders Are Unlocking AI's Full Potential
Historically, the development of artificial intelligence (AI) was controlled by a small circle of pioneering research institutions—those with the financial muscle and computational power to experiment at a scale most could not imagine. Future growth with AI Powered innovation, business growth concept. AI adoption for business. Hand ... More interacts with digital interface displaying growth, innovation and AI investment, productivity icons. Yet the landscape is rapidly changing. Companies are projected to spend over $1 trillion on AI initiatives in the coming years, according to Goldman Sachs. But the question remains: Is scale alone the answer? As access to AI technology expands, so too does the opportunity for transformative, decentralized innovation. However, for small, mid-sized, and even enterprise organizations alike, one thing is becoming increasingly clear: those who leverage structured systems, integrated platforms, and disciplined software strategies will be the ones who maximize AI's true potential. In a world where the prevailing narrative equates bigger models with better outcomes, a quiet revolution is unfolding. "Artificial intelligence does not need bigness to have profound impact," argue Sudarshan Kamath and Akshat Mandloi, fellow tech founders of Their conviction is simple yet disruptive: innovation thrives not through brute computational force, but through smarter, more efficient design. In a mere five months, their startup outpaced billion-dollar competitors in third-party text-to-speech (TTS) metrics—while consuming only a fraction of the resources traditionally deemed necessary. Impressive to say the least. But also validates the size of this market. Their success underscores a profound shift: true AI advantage now lies in precision, efficiency, and flexibility—not in reckless scale. The new frontier of AI demands more than just creativity. It demands structure. Without disciplined systems to organize data, prioritize initiatives, govern decision-making, and scale learnings, even the most promising AI initiatives risk becoming fragmented experiments that never achieve real impact. Small and medium-sized businesses cannot simply throw capital at AI projects; they must out-think, out-structure, and out-adapt. Even enterprises, with more resources at their disposal, are quickly realizing that 'platformization'—the use of connected, adaptive software systems that integrate data, workflows, and intelligence—is the only way to move AI adoption from isolated use cases to enterprise-wide transformation. AI innovation, democratized or otherwise, must live within structured ecosystems that enable repeatability, visibility, and continuous improvement. For decades, innovation tools were gated—reserved for the elite R&D labs of global corporations or those backed by heavy venture capital. Today, cloud computing, open-source technologies, and the rise of platforms like EXCELR8, and others are tearing those walls down. Tools that once required millions now require thousands. Knowledge that once required a graduate degree from MIT is now available to anyone with a Wi-Fi connection and the will to learn. Yet access alone is insufficient. Without scalable frameworks—structured systems for integrating AI insights into daily workflows, decision-making, and strategic execution—organizations risk democratizing noise rather than democratizing progress. As Harvard Business Review aptly notes, "Generative AI can support divergent thinking by producing associations among remote concepts." But without operational systems in place, those associations are fleeting at best, lost at worst. Strategic leadership today demands a delicate balance: encouraging experimentation while insisting on disciplined operational models. As technology leader Tom Berger explains, "IT leadership must provide a governance framework with at least some level of standardization, while also empowering users to be brave and experiment." Without such guardrails, well-meaning AI initiatives quickly fall prey to chaos and inconsistency. In this new era, forward-thinking leaders understand that true innovation is not unbounded; it thrives within intelligent, structured systems that amplify human creativity while preserving focus, discipline, and alignment. As Kamath wisely notes, "We encourage users to experiment, but structure is what allows that experimentation to lead somewhere meaningful." Democratizing AI innovation is not merely a moral imperative—it is a strategic one. The greatest breakthroughs often emerge from unexpected places. But to capture those breakthroughs—to scale them, replicate them, and learn from them—organizations must invest not just in talent, but in platforms, software, and operational systems built for intelligent growth. Small startups. Mid-market challengers. Global enterprises. The playing field is being leveled—but the winners will be those who build or leverage structured ecosystems for agility, insight, and continuous learning. In the end, AI's promise will not be fulfilled by sheer computational horsepower. It will be fulfilled by those who understand a timeless truth: Structure doesn't stifle innovation. It sustains it.
