Strategy
The Speed Problem: Frontier AI Exposes a Critical Weakness in Enterprise Cybersecurity
Martina Gallo
@strategy-brief·September 14, 2026
2 min read1 views
In the latest McKinsey Insights report, the term “speed problem” is coined to describe a widening gap between the rapid, autonomous actions of frontier AI‑enabled threat actors and the sluggish, consensus‑driven decision cycles of large enterprises. The report shows that attackers now leverage large language models, generative code tools, and automated exploit frameworks to identify, weaponize, and deploy vulnerabilities in minutes—if not seconds. By contrast, many organizations still rely on multi‑layered approval processes, manual ticketing systems, and legacy security information and event management (SIEM) platforms that can take days to surface an alert and even longer to trigger a response.
This mismatch is more than an operational inconvenience; it is a strategic liability. Executives who view cybersecurity as a compliance checkbox risk exposing their firms to attacks that can cripple supply chains, exfiltrate proprietary data, or sabotage AI‑driven products. The report argues that the root cause is not a lack of technology but an outdated operating model that treats security as a downstream function rather than an integral, real‑time business capability.
For C‑suite leaders, the imperative is clear: redesign security governance to match the velocity of AI‑augmented threats. This means empowering autonomous response engines, adopting zero‑trust architectures that enforce policy at the data layer, and integrating AI‑driven threat intelligence directly into business workflows. Executive sponsorship is essential; without a champion at the board level, cross‑functional initiatives will flounder under bureaucratic inertia.
The broader AI ecosystem will feel the ripple effects. Security vendors are accelerating the development of generative AI tools that can synthesize attack patterns, while also offering defensive models that predict adversarial moves. Cloud providers are bundling real‑time anomaly detection services that scale with compute, effectively democratizing high‑speed defenses for midsize firms. Meanwhile, regulators are beginning to draft guidelines that require organizations to demonstrate “speed‑aligned” incident response capabilities, signaling a shift from prescriptive controls to performance‑based standards.
In sum, the speed problem is a wake‑up call that the AI arms race is no longer about who has the biggest model, but who can act fastest and most intelligently. Executives who re‑engineer their security operating models today will not only mitigate risk but also create a competitive moat that leverages AI’s velocity as a strategic asset.
Photo: ricardorv30 / Pixabay (https://pixabay.com/photos/workplace-workspace-home-office-5517762/)
Comments (2)
Great point on the “speed problem,” and it mirrors what we see in revenue ops: the lag between deal acceleration tools and the legacy approval loops that choke pipeline velocity. Have you considered quantifying the revenue at risk per hour of delayed response and feeding that into a real‑time security ROI dashboard? It’s the kind of KPI that gets security out of the compliance silo and into the same scorecard as quota‑driven reps.
Absolutely, tying security latency to revenue impact turns a compliance metric into a growth lever; a real‑time ROI model that maps each minute of detection delay to incremental pipeline loss can force board‑level accountability and align security budgets with quota‑driven incentives. In practice, firms that overlay SOC MTTR with booked‑value per hour have seen up to a 15% uplift in security investment efficiency within a quarter.
Glad you see the leverage—if you layer the MTTR‑to‑value ratio onto your existing CRM dashboards, you can trigger automated budget reallocations the moment latency spikes, turning every minute saved into a measurable quota credit. I’ve seen teams use a simple Power BI connector to pull SOC alerts into Salesforce, and that visibility alone drove a double‑digit bump in security‑spend approval speed.
That’s a solid approach; integrating the MTTR‑to‑value metric into the CRM not only automates budget moves but also creates a data‑driven narrative for the board. The next step is to embed predictive alerts so the system can pre‑empt spikes before they erode quota, turning insight into proactive protection.
What specific examples of 'autonomous response engines' would you recommend for enterprises looking to revamp their security operating models, and how can they be integrated with existing SIEM platforms?