Cybersecurity job postings jumped 11 percent in the first quarter of 2026. Executive search firms are turning away clients. Roles that "typically come along every 12 months" are now appearing "every week." Pay packages for top security executives have reached $7 million to $8 million, a figure that, as one recruiter told the New York Times, "would knock someone out of their chair a few years ago."
The cause is straightforward: engineers are using AI to generate code faster than security teams can review it, and that code often contains vulnerabilities. The gap between deployment speed and security review capacity is widening, and organizations are paying dramatically to close it.
This is the hidden cost of DIY AI. And it's one that rarely appears in the business case that got the AI initiative approved.
Why building your own AI solution isn't free
The appeal of building internally is real. A general-purpose model like Claude, a few prompts, some internal documentation, and you have something that looks like a solution. The upfront cost feels low. The control feels high.
What that calculation misses is the ongoing cost of keeping a self-built AI deployment secure, compliant, and trustworthy at scale. Every internal AI tool that touches employee data, business processes, or external communications creates a security surface that needs to be managed. That means someone has to own it, audit it, and update it as the threat landscape changes. As the New York Times reported, leading AI labs have already released models capable of finding and exploiting software vulnerabilities, setting off what one search firm described as a "five-, maybe sevenfold" increase in demand for security executives since last fall.
Organizations that assumed AI deployment was a one-time build are now discovering it's an ongoing operational commitment, with a talent price tag attached.
The buy vs. build calculation has shifted
When companies evaluate vendors, they're often focused on features and price. The security and compliance infrastructure underneath the product rarely gets the attention it deserves, until something goes wrong.
The better frame is to treat vendor selection as an audit. The questions worth asking:
A vendor that has already solved these problems transfers that cost and complexity off your plate. A DIY deployment leaves it on yours, along with the staffing bill that comes with it.
Brian Gaudenti, the security engineer profiled in the New York Times piece, spent months unemployed before upskilling in AI and landing a new role. His observation cuts both ways: "People who are not doing that and waiting for their old jobs to reappear, they're not going to find them again." The same logic applies to organizations. Waiting to think seriously about AI security until the cost becomes acute is a strategy, just not a good one.
What responsible AI deployment actually looks like
Pinnacle has invested in building the security and compliance infrastructure that enterprise AI deployment requires, and documented it in detail in the Pascal for the Enterprise security and compliance paper. SOC 2 Type II certification, GDPR and CCPA compliance, AES-256 encryption at rest, TLS 1.2+ in transit, configurable data retention including zero-day options, and a no-training-on-customer-data policy are all in place. Not because complexity is a virtue, but because deploying AI without that foundation creates costs that show up later, usually when they're much harder to absorb.
The cybersecurity talent bill is climbing because the real cost of AI deployment was underestimated. Auditing the companies building your AI solutions, before you deploy, is how you avoid paying that bill yourself.
vendors beats building internally, and what responsible AI deployment actually requires.
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