CompTIA SecAI+ Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Basic AI Concepts Related to Cybersecurity (17% of the exam)

Compare and contrast various AI types and techniques used in cybersecurity.

• Types of AI including generative AI, machine learning, statistical learning, transformers, deep learning, and various NLP models including LLMs, SLMs, and GANs.
• Model training techniques such as model validation and supervised, unsupervised, and reinforcement learning.
• Fine-tuning methodologies like epochs, pruning, and quantization.
• Prompt engineering including system and user prompts, one-shot, multi-shot, and zero-shot prompting, as well as the use of templates and role-based distinctions.

Summary: This section introduces the foundational knowledge of how different AI techniques are applied to cybersecurity contexts. You will explore how various AI models operate, from traditional machine learning approaches to modern generative and language-based systems. Understanding these models gives you a framework for recognizing how each technique contributes to predictive security capabilities and automated response mechanisms.

It also covers crucial prompt engineering skills that set the foundation for effectively communicating with AI tools. By learning how fine-tuning and model selection impact results, and by understanding the structure of prompts, you gain the ability to guide AI systems in producing more accurate and secure outcomes for cybersecurity applications.

Explain the importance of data security in relation to AI.

• Data processing methods including cleansing, verification, lineage, integrity, provenance, augmentation, and balancing.
• Recognizing data types such as structured, semi-structured, and unstructured data.
• Understanding watermarking, retrieval-augmented generation (RAG), vector storage, and embeddings.

Summary: This section highlights why data quality and protection are central to reliable AI performance. You will examine how data moves through the AI pipeline and how maintaining integrity, provenance, and balance ensures fairness, accuracy, and trustworthiness. Proper data handling prevents model corruption and supports compliance while reinforcing a foundation for responsible AI development.

In addition, you’ll become familiar with advanced data-handling practices like vector storage and retrieval-augmented generation, both of which enhance accuracy in large-scale models. Grasping these concepts helps you align data security practices with organizational privacy goals and AI reliability requirements.

Explain the importance of security throughout the life cycle of AI.

• Stages of AI life cycle such as data collection, preparation, validation, model development and selection, deployment, monitoring, and feedback.
• Incorporation of human-centric AI design principles including human oversight, validation, and the human-in-the-loop concept.

Summary: This section walks through safeguarding AI from creation to operation, ensuring every step aligns with security best practices. You will understand how design, deployment, and maintenance phases work together to uphold integrity and reliability. Continuous monitoring, validation, and human feedback are crucial elements that keep AI systems trustworthy, transparent, and effective.

The emphasis is also on understanding the collaborative interaction between humans and AI systems. By maintaining oversight and structured validation, you ensure that AI models continue to operate ethically and securely while aligning with business and compliance objectives.

Domain 2: Securing AI Systems (40% of the exam)

Given a scenario, use AI threat-modeling resources.

• Frameworks and repositories including OWASP LLM Top 10, OWASP ML Security Top 10, MIT AI Risk Repository, MITRE ATLAS, and CVE AI Working Group references.

Summary: This section focuses on identifying and applying structured models to identify AI system risks. You will explore globally recognized resources that organize vulnerabilities and attack patterns specific to AI environments, helping organizations anticipate and mitigate potential threats before they occur.

Additionally, this knowledge supports developing tailored threat models for organizational use. Understanding industry standards enhances your ability to communicate risk effectively and coordinate mitigation strategies across development and security teams.

Given a set of requirements, implement security controls for AI systems.

• Model and gateway controls including evaluations, prompt guardrails, prompt firewalls, rate and token limits, and endpoint access restrictions.

Summary: You will learn to design and enforce technical safeguards that secure AI systems from manipulation or misuse. Model guardrails and gateway protections are studied so you can maintain controlled inputs, limit excessive use, and ensure model responses remain aligned with policy and compliance expectations.

Learning how to test and validate these controls ensures consistent, secure performance across environments. These concepts position you to develop robust, scalable architectures for deploying AI systems responsibly.

Given a scenario, implement appropriate access controls for AI systems.

• Access management across models, data, agents, networks, and APIs.

Summary: This section emphasizes structuring secure access boundaries around AI components. You will establish how different access layers interact, including model access, data permissions, and network interface security, to maintain confidentiality and prevent unauthorized exposure.

Understanding the interplay among AI subsystems allows you to apply principle-of-least-privilege standards and protect sensitive data. By reviewing access control mechanisms for both dynamic and static resources, you maintain a sustainable defense posture across AI operations.

Given a scenario, implement data security controls for AI systems.

• Encryption at rest, in transit, and in use.
• Data safety mechanisms including anonymization, classification, redaction, masking, and minimization.

Summary: Data is the foundation of secure AI operation, and this section clarifies proactive strategies to ensure its protection. You will study encryption techniques, anonymization practices, and labeling methods that preserve confidentiality without disrupting model functionality.

By understanding data minimization and masking, you can control exposure effectively during training and inference. The guiding principle here is to integrate protection measures seamlessly, so training datasets and outputs remain compliant and secure throughout their life cycle.

Given a scenario, implement monitoring and auditing for AI systems.

• Techniques for prompt and log monitoring, response confidence evaluation, rate tracking, and cost auditing for prompts, storage, and processing.
• Auditing metrics for quality and compliance such as error detection, bias evaluation, and fairness assessments.

Summary: This section introduces continuous supervision methods for maintaining confidence and accountability in AI-driven systems. You’ll learn how to monitor interactions, control usage patterns, and track prompt costs while ensuring responses align with operational intent and policy.

By establishing auditing routines for fairness and accuracy, you reinforce an ethical and compliant AI environment. These practices are essential for demonstrating transparency across the entire AI management framework while ensuring performance reliability.

Given a scenario, analyze the evidence of an attack and suggest compensating controls for AI systems.

• Incident categories including prompt injection, poisoning, jailbreaking, manipulation, and excessive agency.
• Compensating controls such as template restrictions, prompt firewalls, rate limiting, and encryption-based protections.

Summary: This section centers on recognizing and countering attacks that target AI vulnerabilities. You will learn to analyze anomalies, identify evidence of adversarial manipulation, and recommend countermeasures that reestablish safe operation. Attacks such as data poisoning or unauthorized prompt injection are explored to strengthen defensive intuition.

The section also emphasizes compensating controls that both prevent future recurrence and restore secure function fast. By implementing guardrails and data integrity measures, you ensure resilience throughout evolving threat landscapes.

Domain 3: AI-assisted Security (24% of the exam)

Given a scenario, use AI-enabled tools to facilitate security tasks.

• AI-assisted features such as IDE, browser, CLI plug-ins, chatbots, and personal assistants.
• Use cases including signature matching, vulnerability analysis, and automated penetration testing.

Summary: This section helps you understand how to leverage AI-enabled tools for operational efficiency. You will explore how integrated companions like IDE and browser plug-ins streamline coding, analysis, and response activities within cybersecurity workflows. These tools expand the technician’s capacity by providing intelligent recommendations, automation, and fast data interpretation.

Practical applications include vulnerability discovery and anomaly detection, which strengthen proactive defense. By learning to select and manage these solutions, you optimize threat response while improving productivity across security teams.

Explain how AI enables or enhances attack vectors.

• AI-enabled offensive capabilities such as deepfake creation, social engineering, automated reconnaissance, and distributed denial-of-service attacks.

Summary: This section explains how adversaries use AI technology for advanced threat execution. You’ll analyze tactics like automated data correlation, fake content synthesis, and intelligent encryption-breaking to understand new forms of cyber aggression. Recognizing these enhanced attack paths helps you anticipate evolving risks.

By studying AI’s dual-use potential, you gain insight into preventive architecture design and the ethical applications of AI defense. Awareness of adversarial AI techniques allows balanced preparation and fosters innovation while upholding secure standards.

Given a scenario, use AI to automate security tasks.

• Implementing automation in scripting, CI/CD pipelines, incident response, and AI agent operations.

Summary: Automation greatly amplifies security efficiency, and this section explores how AI accelerates repetitive and complex processes alike. You’ll learn to use bots, CI/CD integrations, and low-code tools to improve consistency and speed in software and network protection activities.

AI automation also enhances response coordination and change management, ensuring timely rollout and rollback during security events. Understanding these mechanisms prepares you to integrate AI agility within regulated frameworks for smarter, faster security workflows.

Domain 4: AI Governance, Risk, and Compliance (19% of the exam)

Explain organizational governance structures that support AI.

• Formation of AI governance bodies such as Centers of Excellence and role definitions across data science, machine learning, architecture, and AI security specialties.

Summary: This section outlines how organizations construct frameworks to oversee responsible AI design and management. You’ll study how defined roles like AI security architect, platform engineer, and governance analyst contribute to operational harmony and oversight. Clear policies and structured leadership ensure coordinated compliance and risk mitigation across teams.

Emphasis is placed on creating governance ecosystems that align technical and ethical standards. By understanding each stakeholder’s contribution, you become capable of building compliant and efficient AI operations within complex organizational settings.

Explain risks associated with AI.

• Responsible AI principles covering fairness, reliability, transparency, and explainability.
• Risk areas including bias, data leakage, inaccurate modeling, intellectual property exposure, and unsanctioned AI use.

Summary: Risk management ensures reliability and trust in AI systems. This section focuses on identifying where operational, ethical, or reputational risks could emerge and how to create safeguards that uphold fairness and accountability. Understanding responsible AI principles ensures outcomes remain justifiable and consistent with societal expectations.

Through proactive analysis of potential biases and shadow AI practices, you’ll gain tools for maintaining unbiased, compliant, and transparent AI ecosystems. The section strengthens your ability to balance innovation with accountability.

Summarize the impact of compliance on business use and development of AI.

• Key frameworks including the EU AI Act, OECD standards, ISO initiatives, and NIST (AIRMF).
• Corporate policy aspects involving governance over model use, data privacy, and third-party compliance validation.

Summary: Compliance anchors trust and standardization in the AI field. You’ll study multiple global frameworks that shape the responsible creation and deployment of intelligent systems across industries and geographies. Understanding how these standards apply ensures professional alignment with emerging regulations.

You will also learn how corporate policies around data sovereignty and model categorization integrate with legal requirements. By applying these compliance insights, organizations can innovate confidently while maintaining transparency, control, and integrity.