AI security certifications help cybersecurity professionals understand how to protect artificial intelligence systems from attacks, data exposure, model manipulation, and misuse of automated tools. In 2027, professionals can consider training programmes that cover large language model (LLM) security, generative AI vulnerabilities, AI threat modelling, security testing, and AI governance.
The right programme depends on the work someone wants to pursue. Security engineers and penetration testers may need practical experience identifying and fixing AI vulnerabilities, while security managers and governance professionals may benefit more from training in AI risk management, compliance, and secure AI adoption.
The following five programmes cover different areas of AI security. They include practical technical training, professional certification programmes, and governance-focused credentials.
1. Modern Security AI Security Certification (MSec-CAIS)
Provider: Modern Security
The Modern Security AI Security Certification (MSec-CAIS) is a practical training programme for professionals who want to understand how AI applications are built, tested, attacked, and secured. Its curriculum starts with AI and large language model fundamentals before moving into the security of applications that use RAG, vector databases, AI agents, and external tools.
One useful aspect of the programme is that it covers the architecture of AI applications alongside their security weaknesses. This helps learners understand why certain vulnerabilities occur and where security controls need to be applied.
Topics covered
The curriculum includes several areas of AI application development and security:
AI and LLM fundamentals: Understand artificial intelligence, machine learning, generative AI, and how large language models work.
Embeddings and vector databases: Learn how AI applications store and retrieve information for context-based responses.
Retrieval-Augmented Generation (RAG): Understand RAG architecture and build an internal security chatbot using vector databases.
AI agents and tool calling: Explore how agents interact with external tools, APIs, and other systems.
Prompt injection: Study direct and indirect prompt injection attacks and how attackers manipulate AI application behaviour.
Sensitive information disclosure: Examine how poorly protected AI applications can expose confidential information.
Model backdoors: Understand how malicious behaviour can be introduced into machine learning models.
AI supply chain security: Study model provenance, dependency pinning, model scanning, model signing, and AI Bills of Materials (AIBOMs).
MCP security: Learn how to build MCP servers, identify security weaknesses, and apply defensive controls.
LLM security testing: Practise testing standalone models, REST APIs, and guardrails using tools such as Garak.
The course also includes defensive techniques such as prompt sanitisation, input and output controls, LLM guardrails, MCP gateways, monitoring, and secure AI architecture.
Practical learning approach
The programme uses hands-on labs throughout the curriculum. Learners work with LLM APIs, build RAG applications, explore AI agent workflows, and examine attacks against applications created for the exercises.
The offensive and defensive sections are particularly relevant to professionals who need to assess AI applications before deployment or review existing systems for security weaknesses.
Who should consider this certification?
This programme is suitable for:
Cybersecurity and application security engineers.
Penetration testers and red team professionals.
Developers building AI-powered applications.
Security researchers interested in LLM vulnerabilities.
Technical leaders responsible for AI application security.
The course states that prior AI or LLM knowledge is not required. It begins with foundational concepts and follows a self-paced learning format.
Certification: Modern Security provides a certificate of completion after successful course completion.
What makes it relevant: Its focus on building AI applications and testing their security makes it particularly relevant to professionals who want practical knowledge of LLM security, prompt injection, RAG vulnerabilities, agent security, and AI supply chain risks.
2. PECB Certified Artificial Intelligence Security Professional (CAISP)
Provider: PECB
The PECB Certified Artificial Intelligence Security Professional (CAISP) training course focuses on securing AI systems throughout their life cycle. It covers technical security issues alongside governance, risk management, monitoring, and incident response.
The programme is relevant to professionals who need to assess AI security from the development stage through deployment and ongoing operation. It also examines how organisations can use AI in their own cybersecurity activities, including threat intelligence and security monitoring.
Topics covered
PECB organises the programme around five main competency areas:
Security foundations for AI and machine learning: Understand AI architectures, machine learning systems, deep learning, LLMs, and AI agents.
AI attack techniques and adversarial threats: Study prompt injection, data poisoning, model extraction, and other attacks against AI systems.
AI security controls and defensive operations: Learn how to implement security controls, monitor AI environments, and respond to threats.
AI-enhanced security analysis: Explore AI-assisted threat intelligence, security monitoring, threat hunting, and malware analysis.
AI governance and secure architecture: Understand risk management, regulatory compliance, secure deployment, and AI security programme development.
The curriculum also includes practical exercises involving prompt injection testing, AI red teaming, threat intelligence workflows, and AI-assisted reverse engineering.
Training and examination
The published course agenda covers five days:
Day 1: AI, machine learning, and deep learning fundamentals.
Day 2: LLM architecture, training workflows, and attack techniques.
Day 3: AI security controls and mitigation strategies.
Day 4: Detection, response, and secure deployment.
Day 5: Certification examination.
PECB offers different training delivery formats through its training partners.
Candidates should understand the distinction between passing the examination and receiving the full professional credential. PECB has separate experience and project requirements for its certification levels.
For the full Certified Artificial Intelligence Security Professional credential, its published requirements include five years of professional experience, including two years in information or cybersecurity, and 300 hours of project experience. A provisional credential has different requirements.
Who should consider this certification?
CAISP is relevant to:
AI and machine learning engineers.
Cybersecurity and information security professionals.
SOC analysts and threat intelligence specialists.
Security architects and consultants.
Risk management and compliance professionals.
Technical managers responsible for AI security programmes.
What makes it relevant: The course combines technical AI security with organisational risk management. It can suit professionals who need to understand AI attacks, implement security controls, and contribute to an organisation's AI security programme.
3. NSD Certified AI Security Professional (NCAIS)
Provider: ISAC Foundation | Training delivered by Harikala Future Minds LLP
The NSD Certified AI Security Professional programme focuses on two areas of cybersecurity: protecting AI systems from security threats and using AI tools to support security operations.
The curriculum covers modern AI architectures, LLM security, RAG systems, AI agents, model protection, and AI supply chain security. It also examines how security teams can use AI-assisted tools for threat detection, investigation, vulnerability prioritisation, and incident response.
This combination makes the programme relevant to professionals who work with AI infrastructure as well as those responsible for protecting enterprise IT environments.
Topics covered
The programme is organised into four main learning modules.
1. AI attack surface and LLM security
Learners study the modern AI technology stack and the security weaknesses that can affect its different components. Topics include:
Prompt injection and jailbreak attacks.
Context manipulation and sensitive data disclosure.
Guardrail bypass techniques.
OWASP Top 10 for LLM applications.
Application-level AI security controls.
2. RAG, model, and agentic AI security
This module examines the risks associated with AI applications that retrieve external information or interact with other systems.
It covers RAG poisoning, malicious documents, vector database security, model provenance, memory poisoning, tool misuse, and excessive agent autonomy. It also introduces MCP and Agent-to-Agent (A2A) security.
3. Threat modelling, architecture, and red teaming
The programme introduces established security frameworks and tools used to assess AI systems. These include:
OWASP LLM Top 10.
OWASP Agentic Top 10.
MITRE ATLAS.
CSA MAESTRO.
Microsoft PyRIT.
NVIDIA Garak.
Promptfoo.
Learners examine AI threat modelling, secure AI development practices, AI gateways, runtime controls, and red-team testing.
4. Monitoring, incident response, and AI for security
The final module covers monitoring and responding to threats involving AI applications. Topics include AI telemetry, anomaly detection, agent containment, AI incident forensics, investigation, threat hunting, and detection engineering.
Training format and assessment
The programme is delivered through live instruction, demonstrations, and case-based exercises.
According to the provider's published course information, the training runs for four weeks and includes 18 hours of instruction. Sessions are scheduled on weekends, with two hours on Saturdays and Sundays.
The certification process includes a knowledge assessment and a case-based enterprise scenario. Training is delivered by Harikala Future Minds LLP, while the examination and certification are handled by ISAC Foundation through the National Security Database.
The programme also recommends completing its NCBAI certification before starting NCAIS.
Who should consider this certification?
This programme is relevant to:
SOC analysts and incident responders.
Security architects and security engineers.
Application security professionals.
Penetration testers and red team members.
AI and machine learning engineers.
Cloud security professionals.
Cybersecurity leaders and technology risk professionals.
What makes it relevant: The curriculum covers both AI system protection and AI-assisted security operations. Professionals interested in agentic AI, RAG security, MCP vulnerabilities, and AI-based SOC workflows may find these topics particularly useful.
4. ISC2 Building AI Strategy Certificate
Provider: ISC2
The ISC2 Building AI Strategy Certificate is designed for cybersecurity professionals who want to understand AI adoption, security implications, and organisational risk management.
Its curriculum takes a broader approach than a dedicated technical AI penetration testing course. It covers AI fundamentals, AI security, secure-by-design planning, cybersecurity operations, and global AI regulations.
The programme is particularly relevant to professionals who need to understand how organisations can introduce AI while maintaining appropriate security controls and human oversight.
Topics covered
The certificate consists of six on-demand courses.
1. AI for Cybersecurity
This course introduces the use of AI in cybersecurity. It examines how artificial intelligence can support security activities and discusses AI-specific attacks, mitigation techniques, and security tools.
2. AI Security: Managing Overconfidence
This module addresses the risks of relying too heavily on AI-generated information in security operations. It examines the need for human oversight when using AI for analysis and decision-making.
3. Aligning with Global AI Regulations
Learners explore AI-related standards, security frameworks, and regulatory considerations. The course discusses how organisations can align AI security practices with broader compliance requirements.
4. Foundations of AI
This course introduces AI terminology, technologies, and common applications. It helps learners understand the underlying concepts before examining their security and operational implications.
5. Planning for Secure by Design AI
This module focuses on planning secure AI implementations. It covers the relationship between traditional cybersecurity practices, AI governance, and security throughout the AI life cycle.
6. The Evolving Cybersecurity Workforce
The final course examines how AI affects cybersecurity roles and workforce planning. It addresses the skills and leadership considerations involved in introducing AI into security teams.
Learning format and certification
The certificate is delivered online through on-demand learning. ISC2 lists the programme duration as approximately 16 hours and provides 16 CPE credits.
Learners have 60 days from the purchase date to complete the programme. To receive the completion validation and associated CPE credits for each course, participants must complete the learning material, pass its assessment, and submit the learning evaluation.
Successful participants receive a Credly digital badge and course completion validations.
ISC2 does not list specific prerequisite qualifications for this certificate. Familiarity with cybersecurity principles and practices can help learners understand the material.
Who should consider this certificate?
The programme is suitable for:
Cybersecurity professionals.
Security managers and team leaders.
Information security practitioners.
IT professionals responsible for AI adoption.
Governance, risk, and compliance professionals.
Security professionals interested in AI-related CPE credits.
What makes it relevant: The programme focuses on AI security strategy, organisational planning, and regulatory considerations. It is particularly useful for professionals who need to understand the security implications of AI adoption without focusing exclusively on hands-on AI penetration testing.
5. DSCI Certified AI Governance Professional (DCAGP)
Provider: Data Security Council of India (DSCI)
The DSCI Certified AI Governance Professional (DCAGP) is a governance-focused programme for professionals responsible for AI implementation, security, privacy, and compliance.
As organisations introduce generative AI into products and internal business processes, they need policies that address how AI systems use information, handle sensitive data, and meet applicable regulatory requirements.
This programme examines those responsibilities through AI governance frameworks, responsible AI practices, and the security and privacy considerations of generative AI.
Topics covered
The programme covers several areas of AI governance.
Modern AI technologies and infrastructure
Learners study the development and use of modern AI systems, including:
Generative AI.
Large language models.
Retrieval-Augmented Generation.
LLMOps.
Multi-agent systems.
Large action models.
This section provides an understanding of the technologies that organisations need to govern.
AI security and privacy
The programme covers security and privacy issues associated with generative AI. It also examines how AI can be used in security operations and the risks that organisations need to consider when deploying AI technologies.
AI regulations and compliance
Learners examine the global regulatory environment, including:
European Union AI Act.
United States AI regulatory developments.
United Kingdom AI regulatory approach.
Responsibilities of AI providers and deployers.
Obligations associated with different AI system categories.
Responsible AI adoption
This section covers the principles organisations should consider when developing and deploying AI systems. Topics include transparency, explainability, bias, fairness, reproducibility, and sustainability.
Enterprise AI governance
The programme also addresses the organisational side of AI governance, including policy development, governance interventions, monitoring, evaluation, and AI governance maturity metrics.
Training format and assessment
DSCI describes DCAGP as a two-day, in-person training programme.
The training includes case studies, practical exercises, demonstrations, and laboratory activities. A certification assessment takes place on the second day.
Participants who pass the assessment receive a certification with lifetime validity. DSCI states that the credential is issued as an electronic certificate.
Who should consider this certification?
DCAGP is suitable for:
Chief Information Security Officers (CISOs).
Chief Technology Officers (CTOs).
Chief Data Officers (CDOs).
Data governance professionals.
Privacy and data protection officers.
Governance, risk, and compliance professionals.
Technology architects and product managers.
Security and privacy professionals.
What makes it relevant: The programme focuses on AI governance, regulatory obligations, and responsible AI deployment. It is a suitable option for professionals who oversee organisational AI policies and need to understand the security and privacy responsibilities associated with AI systems.
How to choose the right AI security certification in 2027
Selecting an AI security certification requires some understanding of the skills needed for a particular job. A penetration tester, AI engineer, and security manager may have very different learning requirements.
Here are some factors worth considering before enrolling.
1. Identify the technical skills you want to develop
Start by examining the technical subjects included in the curriculum.
For example, professionals interested in testing generative AI applications should look for training covering prompt injection, indirect prompt injection, RAG security, model vulnerabilities, and AI agent attacks.
Those interested in protecting AI infrastructure should look for courses covering model security, AI supply chain protection, access controls, and secure deployment.
2. Check the practical training included
AI security involves understanding how attacks work and how to prevent them. Reading about vulnerabilities alone may not provide enough knowledge to assess an actual AI application.
Check whether the programme includes practical labs, security demonstrations, threat modelling exercises, or AI red-team activities.
A course that lets learners test vulnerable AI applications can help them understand how weaknesses appear in real implementations.
3. Understand the certification requirements
The word certification can refer to different types of credentials.
Some providers issue a certificate after completing a training programme. Others require candidates to pass an examination and meet additional professional experience requirements.
Before enrolling, check:
Whether an examination is required.
Whether professional experience is necessary.
Whether the certificate has an expiry date.
Whether continuing education credits are available.
Whether the provider issues a professional credential or a course completion certificate.
These differences matter when comparing qualifications for employment or professional development.
4. Match the course with your career goals
Different programmes address different responsibilities.
A penetration tester may benefit from practical AI attack and defence training. An AI engineer may need knowledge of secure model deployment, RAG architecture, and AI supply chain security.
A CISO or GRC professional may need more training in AI governance, regulatory requirements, and organisational risk management.
The course curriculum should match the responsibilities you expect to handle in your role.
Common mistakes to avoid when selecting an AI security certification
Choosing a course based only on its title can lead to an unsuitable learning experience. A few common mistakes are worth avoiding.
Choosing a general AI course instead of security training
A general artificial intelligence course may explain machine learning, neural networks, and generative AI without covering their security weaknesses.
Check whether the curriculum actually includes AI-specific threats, security controls, and vulnerability assessment.
Ignoring hands-on security exercises
Practical experience matters when the objective is to test AI applications. A programme that includes security labs, attack simulations, and defensive exercises can provide a better understanding of how vulnerabilities work.
Confusing completion certificates with professional credentials
A course completion certificate confirms that a learner has completed a programme. A professional certification may require an examination, work experience, or project experience.
Always review the provider's actual certification requirements before making a decision.
Selecting a governance course for a technical security role
Governance training is useful for professionals responsible for compliance and organisational policies. However, someone working in AI penetration testing may need deeper coverage of prompt injection, model attacks, RAG vulnerabilities, and AI red teaming.
Choose according to the technical responsibilities of the intended role.
Conclusion
AI security certifications in 2027 cover several professional areas, from hands-on LLM security testing to AI governance and organisational risk management.
The Modern Security AI Security Certification (MSec-CAIS) focuses on practical AI security, including prompt injection, RAG, AI agents, MCP, model backdoors, and AI supply chain security. PECB CAISP provides a broader professional programme covering AI threats, defensive controls, and governance. The NSD Certified AI Security Professional programme addresses AI infrastructure protection and AI-assisted security operations.
For professionals interested in security strategy and organisational AI adoption, the ISC2 Building AI Strategy Certificate and DSCI Certified AI Governance Professional offer governance-focused learning paths.
Before choosing a programme, review its syllabus, practical exercises, examination requirements, and intended audience. The most suitable certification is the one that develops skills relevant to the security responsibilities you want to take on.