# Cyber Jobs Are Down 26%. Here’s Where They’re Going.

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**Score: 63/100 · skim**

US cybersecurity job postings are down 26% year-over-year, but demand for AI security engineers is up 268%. The video argues that people entering or moving within security should keep their core skills and add the ability to secure LLM apps and AI agents, then prove it with a hands-on project rather than another certification.

- **0:00-0:06** — **The opening statistic.** The speaker contrasts a 26% overall drop in US cybersecurity job postings with one role that grew 268%. This sets up the main point: the field isn't simply shrinking, demand is moving toward a different set of roles. The video never names the data source or the exact time window, so treat the numbers as directional.
- **0:06-0:13** — **Roles losing demand, part 1.** Insider Threat Analyst postings are down 55% and Risk Analyst postings are down 51%. These are among the most analyst-heavy, process-driven jobs, which suggests employers are hiring less for traditional monitoring and governance work. The video doesn't say whether automation, budget cuts, or consolidation is behind the decline.
- **0:13-0:20** — **Roles losing demand, part 2.** Penetration Tester postings are down 46%, and even CISO postings are down 39%. Pen testing has long been a popular goal for newcomers, so this drop matters for anyone planning a career around it. The CISO decline shows the slowdown reaches senior leadership too.
- **0:20-0:26** — **Roles gaining demand.** AI security engineer postings are up 268% and cyber data scientist postings are up 203%. Both combine security with AI or data skills, so the growth sits where the disciplines overlap rather than in classic security silos.
- **0:26-0:31** — **"That's not noise."** The speaker reads the gap between falling and rising roles as a clear signal about where to focus. This is his interpretation. The video gives no absolute posting counts, and a 268% rise from a small base could still mean fewer actual jobs than the shrinking categories.
- **0:31-0:41** — **Fundamentals still count.** Cloud security, identity, and application security (appsec) are still needed. The fastest-growing roles build on top of them, specifically securing LLM apps and AI agents. This caveat matters: the advice is to add AI security to an existing foundation, not to skip the basics and jump straight to AI.
- **0:41-0:51** — **What to learn.** He names three ways LLM apps and agents fail:
  - **Prompt injection:** malicious input that overrides the model's instructions.
  - **Excessive permissions:** agents with more access than they need, which makes any compromise worse.
  - **Unsafe tool use:** agents calling APIs, running code, or taking actions without proper checks.
- **0:51-1:06** — **Build a project, not another cert; ending is a plug.** The speaker recommends one small project showing you can find and fix these problems, because he says that's what employers will ask to see. The last few seconds promote his own course, "Strategies in Secure AI Systems," linked in the description. The plug is disclosed, but it means the speaker has a stake in pushing viewers toward AI security.

**Practical takeaways:**
1. Keep or build core skills in cloud, identity, and appsec first. AI security roles assume you have them.
2. Learn the main ways LLMs and agents fail: prompt injection, excessive agent permissions, and unsafe tool use. The OWASP Top 10 for LLM Applications is a good place to go deeper.
3. Build one small public project: for example, a deliberately vulnerable LLM agent that you attack, then fix with input handling, least-privilege tool access, and checks on tool calls. Document both the attack and the fix.
4. Don't spend your next stretch of study on another general certification. A working project is likely to be a stronger signal for AI security roles.
5. Treat the percentages with caution. They're unsourced and have no absolute numbers, so check them against real job boards before making a major career change.

## Audience signal

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