On May 24th, Felix Rosenqvist passed David Malukas coming to the checkered flag and won the 2026 Indianapolis 500 by 0.0233 seconds, the closest finish in the race’s 110-year history. The razor-thin margin of a half-car length is all that separated first and second after 500 miles of racing. Like many drivers and teams competing, Rosenqvist trusted his systems enough to leave nothing on the table. The difference? He put himself in position for a last-lap maneuver that few expected.
The fastest lap of the day was turned by Conor Daly at over 225 mph, a speed often reached by North America鈥檚 premier open-wheel auto racing series. But for as much research and development is engineered into aerodynamics and the universal chassis, it is the brake that enables these open-cockpit race cars run as fast as they do.
Without confidence in聽the聽ability to control the car under pressure, every extra mile per hour was a gamble聽a driver聽could not afford to take. Reliable brakes are what make high-speed driving possible in the first place. The moment drivers could trust what happened when they needed to slow down, they stopped holding back. The cars got faster,聽and the races got closer. Rosenqvist,聽winning by 0.0233 seconds, demonstrates聽what becomes possible when your control systems are good enough to match your ambition.聽
Cybersecurity plays that same role for AI adoption. The organizations that treat it as infrastructure rather than overhead are the ones that move fastest and go furthest. The ones still waiting until they feel ready enough will keep finding reasons to wait.
The Hesitation Problem
Most enterprises stall on AI because of unresolved security questions. What happens if an employee pastes confidential client data into ChatGPT? What if a prompt injection attack manipulates our internal AI tool? Who owns the output if the model was trained on proprietary data?
These are fair (and common) questions. The problem is that without a security framework to answer them, they become permanent blockers. In the meantime, employees are not waiting. Research consistently shows that a significant majority of knowledge workers are already using commercial AI tools at work, whether those tools have been formally sanctioned. More than half have entered sensitive organizational information into those systems.
Blanket bans do not fix this. They drive usage underground, where it is less visible and harder to govern. Rather than banning AI tools outright, organizations need clear governance, visibility, and guardrails that allow employees to realize AI’s benefits without creating unacceptable risk.
Two Problems Converging at Once
Most organizations we work with are dealing with two distinct AI security challenges at the same time:
The first is shadow AI. Employees are using tools like ChatGPT, Claude, Microsoft Copilot, and Google Gemini in ways that IT and security teams have no visibility into. Confidential data, legal documents, financial information, and intellectual property are being entered into public AI models whose training policies may incorporate user inputs.
The second is deployed AI risk. Many of those same organizations are simultaneously building their own AI applications: internal chatbots, automated workflows, customer-facing tools. These deployments introduce attack vectors that traditional security frameworks were simply not built to manage, including prompt injection, training data poisoning, model inversion, and output manipulation.
Both problems are solvable, although they require different approaches, and neither one goes away on its own.
The Business Case Is Bigger Than Risk Avoidance
When organizations frame AI security as a cost center, they chronically underinvest. The better frame is value creation. AI security is what unlocks business value that is currently frozen behind unanswered questions.
Some of the categories we see consistently:
- Faster AI deployment. Organizations with clear AI security governance compress their security review cycles and move from evaluation to production in weeks rather than months. Every month of faster deployment is a direct productivity and competitive gain.
- Real productivity returns. Research consistently shows that knowledge workers using AI tools see productivity improvements of 20 to 40 percent on relevant tasks. For a 5,000-person organization, even a conservative 20 percent gain represents approximately $9.9 million in annual value. AI security is the infrastructure that makes that gain accessible without the data risk.
- Regulatory readiness. The EU AI Act is now in force. The NIST AI Risk Management Framework has become a de facto standard for federal contractors and much of the commercial market. State-level AI regulations are proliferating. Organizations with documented AI security controls are positioned for compliance; those without facing both regulatory exposure and the cost of reactive remediation.
- Competitive differentiation. In regulated industries and enterprise markets, demonstrable AI governance is increasingly a prerequisite for customer trust and contract awards. That will only become truer as AI adoption expands.
A Practical Path Forward
Building a mature AI security posture does not require doing everything at once. Much like implementing a competent and enterprise-wide Zero Trust approach, it is best to start small and then expand. AI security requires a phased approach that delivers early visibility and risk reduction while building toward something comprehensive and continuously improving.
- Start with visibility and governance (Months 1-3): Inventory all AI tools in active use, both sanctioned and shadow. Assign AI risk ownership to accountable leaders across IT, security, and the business. You cannot govern what you cannot see.
- Secure employee AI access (Months 2-6): Deploy enterprise-grade AI platforms with appropriate data isolation, implement Cloud Access Security Broker controls for commercial AI SaaS applications, and publish an approved AI tool catalog. Give employees good tools with clear guardrails, and they have little reason to use unapproved alternatives.
- Secure deployed applications (Months 3-9): Apply OWASP LLM Top 10 controls to all internally deployed AI applications. Implement prompt injection defenses, output validation, and model access controls. Establish AI-specific logging, monitoring, and incident response playbooks.
- Build continuous assurance (Ongoing): Integrate AI security into your software development lifecycle for all AI projects. Conduct periodic adversarial testing. Map controls to NIST AI RMF and applicable regulatory requirements, and report AI risk posture to executive leadership on a regular cadence.
Stop Deliberating. Start Accelerating.
Every month of AI hesitation represents accumulated opportunity cost. Every delayed project, every unrealized productivity gain, and every competitive advantage surrendered to a peer that moved first, carries a cost.
Organizations that hesitate because of security concerns are not necessarily being cautious; they are falling behind organizations willing to invest in the security infrastructure that makes safe adoption possible. Security and speed are not opposing forces. In fact, getting security right is what enables organizations to move faster with confidence.
Rosenqvist won the 2026 Indianapolis 500 because he trusted his systems completely, and that trust gave him the freedom to leave nothing on the table. The organizations pulling ahead on聽AI聽right now are聽operating聽the same way. Their security investments are exactly what lets them move at full speed.聽The question is not whether to invest in AI. The question聽is whether you have built the foundation聽to drive聽it without hesitation. If聽that is聽the conversation聽you are trying聽to have, WEI is the right place to start it.
