If your goal is to work at the cutting edge of technology, in a job that’s likely to stay in demand, this may be just the role you’re looking for.
While it’s no secret that artificial intelligence jobs are among the highest demand in IT right now, security-related roles are a top priority for. Demand is far ahead of supply, as more organizations rolling out AI or machine learning realize that they need people who know how to secure the technologies. Fortunately for you, the talent pool is still relatively small because the field is so new.
That is great news for job candidates. As AI and ML become more embedded in how companies operate, security will increasingly become a core part of an organization’s broader security strategy. Companies that start building this capability now will be better prepared as those risks become more complex.
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To get a handle on the demand for AI and machine learning security specialists, Spiceworks spoke with Joshua Andrews, assistant professor of computer science and AI management at Brevard College in Brevard, NC; Marc Wilder, vice president of talent solutions at Atrium Global; Serhii Nikolaichuk, founder at financial services firm The Capital Index in Austin, TX; Omair Manzoor, founder at penetration testing firm ioSENTRIX in Regus, VA; and Srinivas Siva, CEO at staffing firm Remotesome.
What an AI security specialist role is all about
AI security specialists assess AI systems for vulnerabilities — such as prompt injection, model manipulation, training data poisoning, unauthorized data exposure, and AI agent containment failures. They test whether AI models can be tricked into leaking sensitive data, bypassing safety controls, or taking unintended autonomous actions. Think of the responsibilities as penetration testing applied to AI systems rather than traditional infrastructure.
The interesting part is that the title is still fuzzy. The work may show up under AI security, application security, ML security, red teaming, security architecture, AI risk, or AI governance.
However the job is categorized, an AI security specialist is part bodyguard, part lie detector and part black-box recorder for a company’s AI.
“The bodyguard decides what the AI may touch,” Nikolaichuk explains. “The lie detector checks whether the model, the data and the machine it runs on are what they claim to be. The black box records what the AI actually did, so that when something goes wrong, nobody has to guess. In practice: red-teaming models, locking down the credentials AI agents hold, securing the model supply chain and keeping logs nobody can quietly edit.”
The fun and exciting aspects to the job
Among the most exciting and rewarding aspects of this job is that the field is still being figured out. That means that as somebody entering it now, you can actually help shape how organizations secure AI instead of just following a playbook somebody wrote ten years ago.
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The work also has a real impact. Organizations are trusting AI with more information and more decisions, and you would help make sure those systems can be used safely and responsibly.
“You’re getting in on the ground floor,” explains Wilder. “This field is so new that the people doing it now are writing the playbook as they go, and that doesn’t happen often in a career.”
Expect a top paycheck and perks
Not surprisingly, you can expect to be handsomely rewarded in this role. The pay potential is especially good because the necessary combination of skills is still very hard to find.
If you genuinely understand both cybersecurity and AI, you can expect base salaries in the $140,000 to $180,000 range. Pay rates for senior and highly specialized roles are going above $200,000 in some markets. But remember, these pay premiums are not awarded for knowing how to use ChatGPT. They are for understanding how AI systems are built, connected, permissioned, deployed, and attacked.
When you look at pay for any IT position, cost of living plays a part, and so does the size of the local talent pool. In some markets only a handful of people can do this work, and companies are willing to pay for that expertise. The industry that you’re in matters as well. Parts of financial services can pay a premium for this job, while in the startup world, equity can be a significant part of the overall package.
Experience that will distinguish you as a candidate
You’ll likely be viewed as a top candidate if you come from traditional penetration testing or application security backgrounds and have layered on AI/ML knowledge. Understanding adversarial machine learning is important, but foundational security skills — thinking like an attacker, understanding trust boundaries, knowing how to chain vulnerabilities — are what separate great AI security specialists from people who just understand the models.
Two archetypes rend to dominate the candidate pool: security engineers who acquired ML literacy, and ML engineers who developed adversarial instincts. Application security, cloud security and red teaming translate most directly. Pure research credentials without production exposure are overvalued.
Degrees in computer science or engineering can help boost your credentials, and certifications can show that you are continuing to build your skills. But companies also have to be realistic about how new this field is. Very few candidates are going to have a decade of direct AI security experience, so employers need to consider potential and adjacent experience, not just what’s already on the résumé.
Skills and traits you should bring to the role
On the technology side, you should have a strong security foundation first. Beyond that, you need to understand how AI tools are built and trained, where the data comes from, and how those tools can be misused. Cloud experience matters too, since so much of this technology runs in cloud environments.
On the business side, it comes down to judgment and communication. You’ll be asked to explain risks to executives who may not be technical, and help determine how much risk the organization is willing to take. You also need to keep up with regulations, since the rules around AI continue to develop. You’ll be among the strongest candidates if you can do both. You should be able to identify the problem and clearly explain why it matters to the organization.
In terms of personal traits that will serve you well in this role, adaptability matters because the risks and tools continue to develop. Integrity is also non-negotiable. You may have deep access to how a company’s AI systems work, flaws and all, so the organization has to be able to trust you.
A heavy dose of curiosity is also very important, as are healthy skepticism and a little professionally useful paranoia. Security people are supposed to ask what happens when someone does something weird with a system. AI provides a lot of new answers to that question. Good judgment matters too. Not every strange AI behavior is a crisis, and not every theoretical attack deserves a massive response.
Certifications that will round out your credentials
Traditional cybersecurity certifications such as Security+, CISSP, or CCSP can help establish your credibility on the security side. Cloud and machine learning certifications can help with the AI side. GIAC and others are also starting to offer certifications focused specifically on AI security. But most organizations would rather see what you have actually done.
For example, show an employer an AI application you threat-modeled. Show them how you tested a RAG system for data exposure. Show them an agent and explain why it has the permissions it has. That tells the employer more than another badge on a résumé.
Tips for succeeding, and growing your career
Build relationships with the data science and engineering teams. That way you’re involved before tools launch, not after, and you become the person they want in the room. Stay hands-on as well. The people who continue testing systems and working directly with the technology keep their skills sharp.
If you do well in this role it can open many career doors because AI security sits in the middle of several different areas. Someone with real experience here could move into security architecture, product security, AI governance, technology risk, security leadership, or more specialized AI security work.
AI security will eventually become less of a separate specialty. Cloud security went through something similar. At one point it felt like its own world. Eventually, cloud became normal enough that security professionals were simply expected to understand it. AI could be headed in the same direction.
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