The application security landscape is about to get exponentially more complex. AI is writing code faster than humans ever could, and with that speed comes vulnerability at industrial scale. Organizations that still rely on manual remediation processes—spending months and thousands of dollars per fix—won't survive 2026's threat landscape.
Those using AI-powered automation to fix vulnerabilities will thrive. Here's what we see coming
With deepfakes and AI-generated content increasingly targeting celebrities, we predict at least one A-list celebrity becomes a vocal cybersecurity advocate in 2026. Taylor Swift seems most likely to turn personal AI attacks into public policy advocacy—and knowing her tendency to process experiences through music, cybersecurity might finally get the pop culture moment it never asked for.
Imagine: "You Belong With Keys" drops, containing the first-ever mainstream lyric about multi-factor authentication. Security Twitter loses its collective mind. CISOs everywhere suddenly have something to bond with their teenagers about. Conference keynotes start with Swift lyrics instead of breach statistics. The phrase "credential stuffing" briefly trends on TikTok.
The serious prediction underneath the humor: As AI-generated deepfakes and impersonation attacks affect high-profile individuals, celebrity advocacy will bring mainstream attention to security issues that have lived in technical obscurity. When someone with 500 million followers talks about AI security risks, boards pay attention in ways that technical reports never achieve. The vulnerability backlog might still be 30 years deep, but at least your CEO will finally understand what "authentication" means.
Supporting Context:
The rise of AI-assisted development—sometimes called "vibe coding"—is about to collide with application security in ways most organizations aren't prepared for. With 70% of organizations already reporting that 41-80% of their code is AI-generated,¹ and research showing that 24.7% of AI-generated code contains security vulnerabilities,² the math is stark: we're creating vulnerable code at an industrial scale.
In April of 2025, Cursor reported that it was generating 1 billion (not million) lines of committed code per DAY. That has only grown.
Consider what this means in practice. If your organization generates 100,000 lines of AI-assisted code in 2026, roughly 25,000 lines will contain security flaws.
Most security teams already can't keep pace with manually-written vulnerabilities—adding this AI-generated flood will push backlogs from "concerning" to "mathematically impossible to address."
Supporting Statistics:
Remember Shadow IT? When employees started using Dropbox and personal Gmail for work files because IT moved too slowly? Shadow AI is the 2026 version, but the risks compound faster, and the governance gaps are wider.
Already, 20% of organizations know that AI code generation tools are being used without authorization—they're explicitly banned, but developers use them anyway.¹ In larger organizations (5,000-10,000 developers), that number rises to 26%.¹ The shadow AI problem mirrors shadow IT from a decade ago, but with a critical difference: when developers use unapproved AI tools, security teams lose visibility into code provenance, making vulnerability tracking and remediation exponentially harder. The code is already in production before anyone knows where it came from.
Supporting Statistics:
This isn't speculation—it's probability. With 57% of organizations already reporting that AI coding assistants have introduced new security risks or made issues harder to detect,³ the ingredients for a high-profile incident are in place. The question isn't if, but when, and whether the organization will be transparent about the root cause.
The breach will likely follow a predictable pattern: an AI assistant confidently generates a code pattern it learned from training data that included vulnerable examples. Perhaps a SQL injection pattern from Stack Overflow circa 2012. Perhaps hardcoded credentials from a public GitHub repo. The code passes review because it looks reasonable, ships to production, and gets exploited.
The post-mortem will reveal that the vulnerability was AI-generated, and suddenly every CISO will be asking uncomfortable questions about their own AI governance.
Supporting Statistics:
The security industry's dirty secret is about to become common knowledge: most organizations fix less than 10% of the vulnerabilities they discover.⁵ The other 90% sit in backlogs, aging like milk. In 2026, the conversation shifts from vanity metrics to outcome metrics.
The math is brutal. Large enterprises often have 10,000+ known vulnerabilities.⁵ At typical remediation rates of 5% annually, spending $10,000 per manual fix, they'll clear their backlog in... never. After 10 years at that pace, assuming 10% annual vulnerability growth, they'll actually be 2,000 vulnerabilities deeper in the hole. Boards and executives are starting to understand this, and they're going to start asking different questions.
Not "how many vulnerabilities did you find?" but "how many did you actually fix, and what's your trajectory?"
Supporting Statistics:
If AI can write code, AI can find holes in code. In 2026, we'll see the first commercially available AI-powered attack tools that continuously probe applications faster than defenders can patch. The backlog stops being a "someday" problem and becomes an "active exploitation" problem.
The Verizon 2024 Data Breach Investigations Report already showed that vulnerability exploitation as an attack vector increased 180% year-over-year.⁶ That trend accelerates when attackers can automate the discovery and exploitation of known vulnerability patterns at scale.
Your 10,000-vulnerability backlog isn't just technical debt anymore—it's a menu. Adversaries using AI don't need sophisticated zero-days when your two-year-old SQL injection vulnerabilities are still sitting unfixed in production.
Supporting Statistics:
This happens with depressing regularity in the security industry, but 2026's version will be notable for the irony. A security vendor—possibly one that sells vulnerability scanning or remediation tools—will suffer a breach. The post-mortem will reveal that the exploited vulnerabilities were sitting in their own backlog, possibly even flagged by their own products, but never prioritized for fixing.
It's not malice; it's the same math everyone else faces. Security vendors have development teams, backlogs, and release pressures just like their customers. The cobbler's children have no shoes. But when it happens to a company that sells the solution to exactly this problem, expect the industry to pause for a collective moment of uncomfortable reflection.
Supporting Statistics:
With 97% of organizations using open source AI models from communities like Hugging Face in the software they build,³ the supply chain attack surface has expanded dramatically. In 2026, someone discovers that a popular model was trained on vulnerable code patterns—and has been confidently suggesting those patterns to developers for months or years.
This isn't theoretical. AI models learn from their training data. If that data includes the millions of vulnerable code examples on public GitHub repositories, the model learns to reproduce those patterns. Unlike a compromised npm package that you can identify and remove, a model that's learned insecure patterns will keep suggesting them indefinitely. The "supply chain attack" for AI doesn't require malicious intent—just insufficiently curated training data.
Supporting Statistics:
Regulators are slow, but they eventually catch up to reality. With AI code generation representing the majority of new code at many organizations, and only 18% having approved tool lists,¹ the governance vacuum is too large to ignore. In 2026, expect the first regulatory guidance specifically addressing AI-generated code.
The treatment will likely mirror how regulators handle third-party libraries and open source components: you're responsible for securing it, regardless of where it came from. "The AI wrote it" will stop being an excuse the same way "the contractor wrote it" stopped being an excuse. Organizations will need to demonstrate they have visibility into AI-generated code, processes to scan and validate it, and documentation of its provenance. For the 20% of organizations already using unauthorized AI tools, this creates immediate compliance exposure.
Supporting Statistics:
Someone will calculate their organization's "years to clear backlog at current remediation rate" and post it publicly. The number will be absurd—30 years, 50 years, never. It will go viral in security circles, get picked up by mainstream tech press, and suddenly every security team will be running the same math for their own organization.
The calculation is straightforward: (Current backlog) ÷ (Annual fix rate - Annual new vulnerability rate) = Years to clear. For organizations fixing 5% annually while adding 10% new vulnerabilities, the answer is "never—you're falling behind." This metric—call it "backlog half-life" or "security debt trajectory"—will become a standard board-level question alongside breach history and insurance premiums.
The organizations that can show improving trajectories will have a story to tell. The ones showing asymptotic growth curves will have uncomfortable conversations.
Supporting Statistics:
Got your own predictions? We'd love to hear them—especially the ones that make us look completely wrong by December.