I’m thrilled to sit down with Simon Glairy, a trailblazer in insurance and Insurtech, whose expertise in risk management and AI-driven risk assessment has guided countless organizations through the evolving landscape of cyber threats. With a sharp eye on emerging technologies, Simon has been at the forefront of addressing the complexities introduced by innovations like Model Context Protocol (MCP). In our conversation, we explore the unprecedented risks MCP poses to insurers, the systemic vulnerabilities it creates across digital supply chains, and actionable strategies for navigating this uncharted territory of cyber risk.
How did you first come across the emerging threat of Model Context Protocol (MCP), and what made you realize it could be a game-changer for cyber risk in the insurance industry?
I first stumbled upon MCP while consulting with a large financial institution about two years ago, as they were integrating generative AI tools into their operations. They were ecstatic about the efficiency gains, but I noticed something unsettling—their AI models were directly connected to critical infrastructure with almost no safeguards. It hit me like a cold splash of water: this connectivity, while powerful, was a gaping doorway for cyber attackers. After digging deeper with my team, we uncovered that MCP, as this connective layer, wasn’t just a niche issue—it was quietly embedding itself across industries, creating risks that insurers hadn’t even begun to quantify. I remember late nights poring over security logs, realizing that a single misconfigured access control could unravel entire portfolios. That’s when I knew we were stepping into an uncharted era of cyber risk that demanded urgent attention.
Can you paint a picture of how MCP’s integration into digital infrastructure creates systemic vulnerabilities, and perhaps share an example or scenario that illustrates its potential impact?
Absolutely, MCP’s role as a bridge between AI models and an organization’s digital ecosystem is both its strength and its Achilles’ heel. Imagine a scenario where a multinational retailer uses MCP to link their AI-driven inventory system with suppliers and customer databases in real time—it’s seamless until a flaw in the MCP setup, like overly broad permissions, is exploited. I’ve seen a similar case where a misconfigured MCP server allowed attackers to send malicious queries, extracting sensitive customer data as if they were legitimate users. Within days, the breach cascaded to connected partners in the supply chain, impacting not just one company but several insureds under the same portfolio. The emotional toll was palpable—executives were in shock, realizing their cutting-edge tech had turned into a liability overnight. This kind of systemic vulnerability means insurers can’t just look at individual clients anymore; they have to map out shared dependencies to truly grasp the exposure.
With MCP exposure spreading through digital supply chains, how pervasive do you think this risk is right now, and what can companies do to get a handle on their own vulnerabilities?
I’d say MCP exposure is far more pervasive than most realize—it’s like a silent undercurrent running through digital supply chains. From my vantage point, having worked with dozens of firms, I estimate that a significant portion of companies adopting generative AI solutions are unknowingly incorporating MCP, especially in sectors like finance and retail where real-time integration is king. I recall a mid-sized logistics firm I advised last year; they had no idea their new AI tool was interfacing with suppliers via MCP until we ran a deep audit—it was a nerve-wracking revelation for their IT team, who felt blindsided by the hidden connections. To assess their own exposure, companies need to start with a thorough inventory of all AI integrations, mapping out where and how MCP might be facilitating connections. Then, they should prioritize security audits focusing on access controls and permissions, tightening them to the bare minimum needed. It’s not glamorous work, but it’s like checking the locks on every door—neglect it, and you’re inviting trouble.
What challenges does the rapid evolution of MCP-enabled tools pose for insurers in terms of risk selection, and how can they adapt to keep pace with these changes?
The breakneck speed at which MCP-enabled tools evolve is a nightmare for risk selection in insurance—it’s like trying to hit a moving target in a storm. Traditional risk profiles can become obsolete in months, as new vulnerabilities emerge with every update or integration. I remember working with an insurer who thought they had a solid handle on a client’s cyber risk, only to discover that a new MCP-linked AI tool had been deployed without any security vetting, completely shifting the exposure overnight—it was a humbling moment for their underwriting team. To adapt, insurers need to shift from static assessments to dynamic, data-rich models that incorporate real-time cyber intelligence. This means partnering with tech specialists to track MCP usage trends and embedding continuous monitoring into their processes. It’s a heavy lift, but I’ve seen insurers start to integrate richer data points into their risk selection, allowing them to spot red flags before they turn into claims. The key is staying proactive—waiting for the next breach to update policies just won’t cut it.
When it comes to managing MCP risks, what does effective portfolio monitoring look like, and can you walk us through a practical approach to implementing it?
Effective portfolio monitoring for MCP risks is all about vigilance and granularity—it’s not enough to check in once a year and call it a day. It’s about continuously tracking the cyber health of every insured in your portfolio, with a specific lens on AI integrations and shared dependencies. A practical approach starts with deploying automated tools that scan for MCP-related vulnerabilities across clients’ digital ecosystems, flagging issues like misconfigured servers or unusual data flows in real time. Next, insurers should build a dashboard to visualize exposure across portfolios, linking individual risks to broader supply chain connections. I worked with a forward-thinking insurer who implemented this step-by-step, and within six months, they identified a critical MCP flaw in a client’s system that could have impacted multiple insureds—they mitigated it before it became a catastrophe, saving millions in potential claims. Finally, it’s crucial to pair this tech with human expertise, regularly reviewing findings and refining policy wordings to cover AI-specific incidents. It’s exhausting work, but seeing a looming disaster averted makes every late-night analysis worthwhile.
Looking ahead, what is your forecast for the future of MCP-related cyber risks in the insurance industry, and how do you see the landscape evolving over the next few years?
I believe we’re just at the tip of the iceberg with MCP-related cyber risks, and the next few years will be a crucible for the insurance industry. As more organizations adopt AI solutions with MCP underpinnings, I foresee a sharp rise in systemic vulnerabilities—think of it as a web of interconnected risks growing denser by the day. Without aggressive action, we could see breaches that dwarf current incidents, impacting entire sectors through shared digital supply chains; I’ve already got sleepless nights imagining the fallout. On the flip side, I’m hopeful that insurers will rise to the challenge by embracing continuous monitoring and richer data analytics, creating more resilient risk models. We’ll likely see tighter regulations around AI integrations as well, which could force better security standards. Ultimately, the landscape will evolve into a battleground where proactive adaptation separates the survivors from the casualties—I just hope the industry moves fast enough to stay ahead of the curve.
