What does a AI/ML Security Engineer do?
An AI/ML Security Engineer secures the machine-learning systems a company builds or integrates: model training pipelines, inference endpoints, prompt surfaces, and the data stores that feed and audit them. The role is new and drifting fast. You work with data scientists and ML engineers who think about model quality; you bring the adversarial lens. Prompt injection, training-data poisoning, model extraction, jailbreaking, and agent-tool abuse are the threat model. Good AI/ML security engineers read research papers, implement concrete mitigations, and do not let vendor promises replace evidence.
A day in the role
Thursday, 9:30 AM. Threat model a new agent-based feature with tool access to internal APIs. You flag three ways an attacker could chain prompt-injection plus tool-call into data exfiltration and propose concrete tool-scope restrictions. Mid-morning you run a Garak red-team session against the production LLM endpoint; two jailbreaks work, you file them and propose a safety-layer fix. Lunch reading the latest paper on indirect prompt injection. Afternoon you partner with the data-science team on a training-data-poisoning defense pattern. By 4:30 PM you draft the AI security review checklist for the next product launch.
Core responsibilities
- Threat-model LLM and ML-powered features (prompt injection, tool abuse, data leakage)
- Review retrieval-augmented-generation (RAG) and agent architectures for security boundaries
- Secure model training pipelines against data-poisoning and supply-chain attacks
- Test model endpoints against jailbreaks, prompt injections, and extraction attacks
- Partner with legal and privacy on data-provenance and training-data governance
- Monitor production model traffic for abuse patterns (prompt injections, rate anomalies)
- Integrate AI-safety controls (moderation, output filtering, rate limits) with developer velocity
- Stay current with OWASP LLM Top 10, NIST AI RMF, and academic adversarial-ML research
Key skills
Tools you will use
MITRE ATT&CK tactics this role touches
Common pitfalls
- Treating prompt injection as solvable with a clever system prompt
- Giving an agent broad tool access without thinking through capability chaining
- Skipping the moderation + output-filter layer because 'the model is safe'
- Trusting a provider's advertised safety features without testing them against the specific use case
Where this leads
Natural next roles for experienced AI/ML Security Engineers.
Which certifications does a AI/ML Security Engineer need?
Professionals in this role typically hold or pursue these cybersecurity certifications. Visit our certification guides for cost, exam details, and career impact analysis.
Exam-ready prep for the certs this role names
1 add-on · from $97The DecipherU career guide tells you which certifications the AI/ML Security Engineer path values. Each entry below is scenario practice for one of those exams, one domain at a time, with the primary source cited after every answer.
Built from federal labor data (Bureau of Labor Statistics, O*NET) and security threat frameworks (MITRE ATT&CK), with industry job-board data layered on top. Editorial review by Julian Calvo, Ed.D., M.S..
How much does a AI/ML Security Engineer make?
Salary estimates for AI/ML Security Engineer roles. Based on BLS OES median ($169,700) with experience-tier ratios derived from BLS OES percentile patterns for cybersecurity occupations, May 2024. Actual compensation varies by location, employer, and certifications. Source: BLS OES
Career progression
Entry
SOC Analyst I
0–2 yrs
Mid
AI/ML Security Engineer
3–6 yrs
Senior
Sr. Security Engineer
7–12 yrs
Principal
Principal Engineer
12+ yrs
Typical progression timeline. Advancement varies by organization, sector, and individual performance. Based on industry career trajectory data.
Personality fit (RIASEC)
The radar maps this role's top RIASEC dimensions to the Holland Code occupational profile published by O*NET, the US Department of Labor's occupational information network. Realistic-Investigative-Conventional patterns dominate technical cybersecurity roles; Enterprising-Social-Investigative patterns dominate sales and leadership tracks.
Holland Code fit based on O*NET occupational profile and DecipherU career data. Take the full RIASEC assessment →
How do I become a AI/ML Security Engineer?
Start by exploring the interview questions for this role, reviewing salary data by location, and taking the RIASEC career assessment to confirm this path matches your personality profile. Use the links below to access each resource.
Career resilience: AI/ML Security Engineer
Recession risk
Very Low
Cybersecurity employment grew through every downturn since 2008. Source: BLS OES historical data.
AI impact
Augments (not replaces)
AI automates alert triage but expands attack surface, creating more specialized roles.
Regulatory demand
SOX, HIPAA, PCI-DSS, and SEC cyber disclosure rules legally require security teams regardless of economic conditions.
Government/defense demand
Federal and defense contractor roles for this function carry 15-25% salary premiums and strong job security.
Cybersecurity is one of the few technical fields where employment has grown through every recession since BLS began tracking it. The data across four economic downturns shows a consistent pattern: demand surges during crises, not during booms.
Salary data is compiled from public sources including the Bureau of Labor Statistics and industry surveys. Actual compensation varies by location, experience, company, and negotiation. This information is for educational purposes only and does not constitute financial advice.
Certification details are sourced from official certifying body websites. Verify current pricing, exam format, and requirements directly with the certifying organization before making decisions. DecipherU is not affiliated with any certifying body.