- How AI Is Changing Cybersecurity and Creating New Career Paths
- AI is changing what Cybersecurity teams need to protect
- Why AI security requires more specialized expertise
- Eight emerging AI security roles and what they involve
- How to Transition From Cybersecurity to AI Security
- The next career move is about choosing the right specialization
TL;DR
AI is changing cybersecurity by introducing new risks across AI models, agents, RAG systems, and automated workflows. This shift is creating eight emerging AI security specializations across engineering, offensive security, threat intelligence, security operations, and governance. Professionals in cybersecurity, cloud, software development, and risk management already have transferable skills. Choosing the right career path depends on your existing expertise, coding proficiency, and the AI security skills you want to develop.
How AI Is Changing Cybersecurity and Creating New Career Paths
AI is changing cybersecurity in two distinct ways: attackers are using AI to scale malicious activities, and organizations are deploying AI systems that need protection. According to IBM's Cost of a Data Breach Report 2026, one in four malicious breaches examined in its research was AI-enabled. These breaches cost organizations an average of $6 million.
At the same time, AI models, agents, and automated workflows introduce security considerations involving sensitive data, system permissions, and interactions with external tools.
For professionals in cybersecurity, cloud engineering, software development, machine learning, and risk management, understanding these risks can open new areas of specialization.
Professionals looking to strengthen their core security expertise can explore the Cybersecurity course from Johns Hopkins University, which covers network and infrastructure security, cloud and data security, risk management, and AI-powered cyber defense.
JHU Cybersecurity Certificate Course
Build in-demand cybersecurity skills with JHU experts. Learn AI, cloud, and network security. Hands-on projects. Flexible online format.
AI is changing what Cybersecurity teams need to protect
Traditional cybersecurity focuses on securing networks, applications, endpoints, infrastructure, identities, and data. Those responsibilities remain important, but AI introduces additional systems and behaviors requiring security oversight.
Consider an organization deploying an AI-powered customer support agent. The agent retrieves internal documents, accesses customer records, and interacts with business applications.
Securing this environment involves more than protecting servers and APIs. Security teams must establish which information the agent is authorized to retrieve, which actions the agent is permitted to perform, and how to prevent untrusted content from manipulating its behavior.
These concerns introduce several AI-specific security risks:
- Prompt injection: Malicious instructions attempt to manipulate an AI system through user inputs or external content.
- RAG-related data poisoning: Attackers manipulate documents, knowledge sources, or retrieval data so an AI application retrieves misleading or malicious content. The impact depends on the application's retrieval controls, permissions, and connected tools.
- Excessive agency: An AI system receives more permissions, functionality, or autonomy than necessary.
- Sensitive information disclosure: Inadequate controls expose confidential information through an AI application's inputs, processing, or outputs.
The OWASP Top 10 for LLM Applications identifies these concerns among the security risks associated with large language model applications.
Core security principles still apply to AI systems. Access control, threat modeling, secure development, and continuous monitoring must extend across models, retrieval pipelines, agents, and connected tools.
Why AI security requires more specialized expertise
The growing use of AI is changing the skills cybersecurity professionals may need to apply in their roles.
The 2025 ISC2 Cybersecurity Workforce Study found that 73% of respondents believe AI will create a need for more specialized cybersecurity skills. Another 72% expect a greater need for strategic cybersecurity thinking.
These findings reflect the expectations of cybersecurity professionals, rather than measured growth in specific job vacancies.
The skills required depend on the security function: application security professionals may assess AI application vulnerabilities, machine learning engineers may protect model artifacts and deployment pipelines, and security operations professionals may use AI-assisted investigations.
As these responsibilities expand, professionals can build specialized expertise through the AI Security course Johns Hopkins University, which explores AI threat modeling, LLM and RAG security, agentic AI systems, runtime guardrails, and AI governance.
Johns Hopkins Certificate Program in AI Security
Build AI security skills for LLMs, RAG, and AI agents. Learn threat modeling, secure architectures, runtime guardrails, and AI governance with Johns Hopkins University.
These responsibilities span four broad career paths: building AI securely, protecting and testing AI systems, defending with AI, and governing AI responsibly.
Eight emerging AI security roles and what they involve
The following eight representative roles illustrate how AI security responsibilities can be organized across four career paths. These titles are not a universal industry classification; responsibilities and job titles may vary by employer.
| Career path | Emerging AI security role | What you'll work on | Relevant professional background |
| 1. Build AI securely | Forward-Deployed AI Security Engineer | Securing AI applications, APIs, agent permissions, RAG workflows, and production deployments | Software, cloud, platform, or security engineering |
| AI Security Architect | Designing secure AI architectures, trust boundaries, access controls, and data flows | Security, cloud, or solutions architecture | |
| 2. Protect and test AI systems | AI/Model Security Specialist | Protecting models, training data, deployment pipelines, and AI infrastructure | ML engineering, MLOps, data engineering, or security engineering |
| AI Penetration Tester | Identifying vulnerabilities in prompts, RAG systems, APIs, and agent tools | Penetration testing, ethical hacking, AppSec, or security research | |
| AI Red Team Specialist | Simulating adversarial attacks, jailbreaks, tool misuse, and agent failures | Red teaming, penetration testing, security research, or ML security | |
| 3. Defend with AI | AI Threat Intelligence Engineer | Tracking AI-enabled attacks and strengthening threat intelligence, hunting, and detection | Threat intelligence, SOC, detection engineering, or malware research |
| AI-Enabled SOC Lead | Using AI-assisted workflows for alert prioritization, investigations, detection, and incident response | SOC operations, SOC management, or detection engineering | |
| 4. Govern AI responsibly | AI Governance & Risk Specialist | Establishing AI policies, risk controls, accountability, oversight, and compliance practices | GRC, privacy, risk, compliance, or cybersecurity leadership |
Which AI security role fits your technical background?
Explore the coding requirements, core technical stacks, and relevant experience for emerging AI security roles.
Download the AI Security Career Map
Get a complete breakdown of in-demand roles, key skills, requirements, and step-by-step transition roadmaps.
What these roles mean for your career
Choosing an AI security specialization starts with understanding where your existing experience fits.
Software, cloud, and security engineers are well positioned to explore secure AI architecture and deployment. Penetration testers and security researchers have a foundation for assessing prompt injection, insecure tool access, and other AI application vulnerabilities.
Professionals in threat intelligence and security operations might extend their experience into AI-enabled threat detection, investigations, and response. Those working in risk, privacy, and compliance might focus on AI governance, accountability, and oversight.
How to Transition From Cybersecurity to AI Security
The most suitable AI security specialization depends on your existing expertise, the systems you understand, and the security problems you want to solve. Rather than learning every AI tool or framework, focus on the skills that complement your current experience and prepare you for a specific role.
Use the following comparison to identify a potential career path and the additional skills you may need to develop.
| Your Current Expertise | Skills to Build for AI Security | Potential Specialization |
|---|---|---|
| Software or cloud engineering | Python, LLM application architecture, IAM, API security, RAG, secure deployment | Forward-Deployed AI Security Engineer |
| Security or solutions architecture | AI threat modeling, trust boundaries, agent permissions, secure RAG architecture | AI Security Architect |
| ML engineering or MLOps | Model security, ML pipelines, secrets management, LLMOps, model monitoring | AI/Model Security Specialist |
| Penetration testing or AppSec | Prompt injection testing, RAG poisoning, API abuse, agent tool security | AI Penetration Tester |
| Red teaming or security research | Adversarial testing, jailbreak evaluation, threat modeling, agent misuse | AI Red Team Specialist |
| Threat intelligence or detection engineering | AI-enabled threat analysis, MITRE ATT&CK, detection engineering, malware analysis | AI Threat Intelligence Engineer |
| SOC operations or management | SIEM, SOAR, AI-assisted investigations, security automation | AI-Enabled SOC Lead |
| Risk, privacy, or compliance | AI risk frameworks, governance, model oversight, audit, control mapping | AI Governance & Risk Specialist |
These are potential career transitions, not fixed eligibility requirements. The most useful next step is to choose a specialization, identify the skills missing from your current experience, and develop practical expertise in those areas.
The next career move is about choosing the right specialization
The next step in an AI security career is identifying which security problems you want to solve and where your existing expertise provides an advantage.
Whether your background is in engineering, threat detection, penetration testing, or governance, building expertise in the relevant AI systems and security risks is more useful than attempting to learn every tool or framework.
Start with one specialization, understand its technical requirements, and develop hands-on experience with the security challenges involved.
Explore the AI security career paths in greater detail
Great Learning's AI Security Career Guide provides a detailed breakdown of eight emerging roles, including their responsibilities, technical stacks, relevant career backgrounds, and coding requirements.
Download the AI Security Career Map to see how your current skill set maps to emerging roles.
