New CompTIA SecAi+ CY0-001 Exam Questions and Answers

CompTIA SecAI+ Beta | comptia secai+ cy0-001 | CompTIA Sec AI+ Prep Course material |

Exam: CompTIA SecAI+ Beta

CompTIA SecAI+ is the first certification in our new expansion series, designed to help you secure, govern, and responsibly integrate artificial intelligence into cybersecurity operations. Gain the skills to defend AI systems, meet global compliance standards, and use AI to enhance threat detection, automation, and innovation while strengthening organizational resilience. CompTIA SecAI+ questions and answers are scheduled to launch on February 17, 2026.

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What is the CY0-001 focused on?

The CY0-001 exam or as it’s also known, the CompTIA SecAI+ Beta, like all tests, there is a bit of freedom on CompTIA's part to exam an array of subjects. That means knowing the majority of CY0-001 content is required because they test randomly on the many subjects available. Be aware too that experience requirements often exist because they’ve observed the average person and what is required. You can always push past that to succeed with the CY0-001 but it may take some extra work.

Please follow these Practice Test Topics:

SecAI+ (V1) exam objectives summary

Basic AI concepts related to cybersecurity (17%)

• Explain core AI principles and terminology: Machine learning, deep learning, natural language processing, and automation.

• Identify AI applications in security: Use cases for AI in threat detection, defense, and security operations.

• Recognize AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use of generative AI.

Securing AI systems (40%)

• Implement security controls: Protect AI systems, data, and models using robust technical safeguards.

• Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures.

• Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers.

AI-assisted security (24%)

• Enhance detection and response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation.

• Automate security workflows: Integrate AI for event triage, alert correlation, and response orchestration.

• Apply AI techniques in operations: Incorporate AI into threat modeling, behavior analysis, and continuous monitoring.

AI governance, risk, and compliance (19%)

• Understand regulatory frameworks: Identify global governance requirements and their implications for AI adoption.

• Integrate GRC into AI projects: Incorporate governance, risk management, and compliance practices throughout the AI lifecycle.

• Ensure responsible AI use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF.

Skills learned

Identify various types of threats, attacks, and vulnerabilities, including malware, social engineering, and application attacks.

Utilize security technologies and tools, such as firewalls, intrusion detection systems, and endpoint security, to protect systems.

Design secure network architectures, implement secure systems, and apply secure protocols for architecture and design.

Manage identity and access concepts, including authentication, authorization, and accounting, to ensure secure access control.

Assess and manage risk through risk analysis, mitigation strategies, and business continuity planning.

Apply cryptography concepts, including encryption algorithms, public key infrastructure (PKI), and digital signatures, to secure data.

Implement compliance and operational security measures, including security policies, procedures, and best practices.

CompTIA SecAI+

Hardware and Software List

CompTIA has included this sample list of hardware and software to assist candidates as they prepare for the SecAI+ CY0-001 V1 certification exam. This list may also be helpful for training companies that wish to create a lab component for their training offering. The bulleted lists below each topic are sample lists and are not exhaustive.

EQUIPMENT

• Laptops

• Cloud VMs

• Graphics processing units (GPUs)

• NVidia Jetson Nano Orin

• Mobile devices

• Sandbox environment

• Local area network (LAN)

SOFTWARE

• Virtual containers

• Large data sets

• Test data sets

• Python environment

• R environment

• IDE

• Jupyter environment

• Chatbots

• LLMs

• Open-source tools

o GitHub

o Ollama

• Cloud-based environment

• Cloud-based AI studios

• Vector database

• NoSQL Database

• Neo4j Graph Database