CCSP Certification Exam Outline

1.1 - Understand cloud computing concepts

• Cloud computing definitions
• Cloud computing roles and responsibilities (e.g., cloud service customer, cloud service provider, cloud service partner, cloud service broker, regulator)
• Key cloud computing characteristics (e.g., on-demand self-service, broad network access, multi-tenancy, rapid elasticity and scalability, resource pooling, measured service)
• Building block technologies (e.g., virtualization, storage, networking, databases, orchestration)

1.2 - Describe cloud reference architecture

• Cloud computing activities
• Cloud service capabilities (e.g., application capability types, platform capability types, infrastructure capability types)
• Cloud service categories (e.g., Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS))
• Cloud deployment models (e.g., public, private, hybrid, community, multi-cloud)
• Cloud shared considerations (e.g., interoperability, portability, reversibility, availability, security, privacy, resiliency, performance, governance, maintenance and versioning, service levels and service-level agreements (SLA), auditability, regulatory, outsourcing)
• Impact of related technologies (e.g., data science, machine learning, artificial intelligence (AI), blockchain, Internet of Things (IoT), containers, quantum computing, edge computing, confidential computing, DevSecOps)

1.3 - Understand security concepts relevant to cloud computing

• Cryptography and key management
• Identity and access control (e.g., user access, privilege access, service access)
• Data and media sanitization (e.g., overwriting, cryptographic erase)
• Network security (e.g., network security groups, traffic inspection, geofencing, zero trust network)
• Virtualization security (e.g., hypervisor security, container security, ephemeral computing, serverless technology)
• Common threats
• Security hygiene (e.g., patching, baselining)

1.4 - Understand design principles of secure cloud computing

• Cloud secure data lifecycle
• Cloud-based business continuity (BC) and disaster recovery (DR) plan
• Business impact analysis (BIA) (e.g., cost-benefit analysis, return on investment (ROI))
• Functional security requirements (e.g., portability, interoperability, vendor lock-in)
• Security considerations and responsibilities for different cloud categories (e.g., Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS))
• Cloud design patterns (e.g., SANS security principles, Well-Architected Framework, Cloud Security Alliance (CSA) Enterprise Architecture)
• DevOps security

1.5 - Evaluate cloud service providers

• Verification against criteria (e.g., International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 27017, Payment Card Industry Data Security Standard (PCI DSS))
• System/subsystem product certifications (e.g., Common Criteria (CC), Federal Information Processing Standard (FIPS) 140-2)

2.1 - Describe cloud data concepts

• Cloud data life cycle phases
• Data dispersion
• Data flows

2.2 - Design and implement cloud data storage architectures

• Storage types (e.g., long-term, ephemeral, raw storage)
• Threats to storage types

2.3 - Design and apply data security technologies and strategies

• Encryption and key management
• Hashing
• Data obfuscation (e.g., masking, anonymization)
• Tokenization
• Data loss prevention (DLP)
• Keys, secrets and certificates management

2.4 - Implement data discovery

• Structured data
• Unstructured data
• Semi-structured data
• Data location

2.5 - Plan and implement data classification

• Data classification policies
• Data mapping
• Data labeling

2.6 - Design and implement Information Rights Management (IRM)

• Objectives (e.g., data rights, provisioning, access models)
• Appropriate tools (e.g., issuing and revocation of certificates)

2.7 - Plan and implement data retention, deletion, and archiving policies

• Data retention policies
• Data deletion procedures and mechanisms
• Data archiving procedures and mechanisms
• Legal hold

2.8 - Design and implement auditability, traceability, and accountability of data events

• Definition of event sources and requirement of event attributes (e.g., identity, Internet Protocol (IP) address, geolocation)
• Logging, storage and analysis of data events
• Chain of custody and non-repudiation

3.1 - Comprehend cloud infrastructure and platform components

• Physical environment
• Network and communications
• Compute
• Virtualization
• Storage
• Management plane

3.2 - Design a secure data center

• Logical design (e.g., tenant partitioning, access control)
• Physical design (e.g., location, buy or build)
• Environmental design (e.g., Heating, Ventilation, and Air Conditioning (HVAC), multi-vendor pathway connectivity)
• Design resilient

3.3 - Analyze risks associated with cloud infrastructure and platforms

• Risk assessment (e.g., identification, analysis)
• Cloud vulnerabilities, threats and attacks
• Risk mitigation strategies

3.4 - Plan and implementation of security controls

• Physical and environmental protection (e.g., on-premises)
• System, storage and communication protection
• Identification, authentication and authorization in cloud environments
• Audit mechanisms (e.g., log collection, correlation, packet capture)

3.5 - Plan business continuity (BC) and disaster recovery (DR)

• Business continuity (BC) / disaster recovery (DR) strategy
• Business requirements (e.g., Recovery Time Objective (RTO), Recovery Point Objective (RPO), recovery service level)
• Creation, implementation and testing of plan

4.1 - Advocate training and awareness for application security

• Cloud development basics
• Common pitfalls
• Common cloud vulnerabilities (e.g., Open Web Application Security Project (OWASP) Top-10, SANS Top-25)

4.2 - Describe the Secure Software Development Life Cycle (SDLC) process

• Business requirements
• Phases and methodologies (e.g., design, code, test, maintain, waterfall vs. agile)

4.3 - Apply the Secure Software Development Life Cycle (SDLC)

• Cloud-specific risks
• Threat modeling (e.g., Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege (STRIDE), Damage, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD), Architecture, Threats, Attack Surfaces, and Mitigations (ATASM), Process for Attack Simulation and Threat Analysis (PASTA))
• Avoid common vulnerabilities during development
• Secure coding (e.g., Open Web Application Security Project (OWASP) Application Security
• Verification Standard (ASVS), Software Assurance Forum for Excellence in Code (SAFECode))
• Software configuration management and versioning

4.4 - Apply cloud software assurance and validation

• Functional and non-functional testing
• Security testing methodologies (e.g., blackbox, whitebox, static, dynamic, Software Composition Analysis (SCA), interactive application security testing (IAST))
• Quality assurance (QA)
• Abuse case testing

4.5 - Use verified secure software

• Securing application programming interfaces (API)
• Supply-chain management (e.g., vendor assessment)
• Third-party software management (e.g., licensing)
• Validated open-source software

4.6 - Comprehend the specifics of cloud application architecture

• Supplemental security components (e.g., web application firewall (WAF), Database Activity Monitoring (DAM), Extensible Markup Language (XML) firewalls, application programming interface (API) gateway)
• Cryptography
• Sandboxing
• Application virtualization and orchestration (e.g., microservices, containers)

4.7 - Design appropriate Identity and Access Management (IAM) solutions

• Federated identity
• Identity providers (IdP)
• Single sign-on (SSO)
• Multi-factor authentication (MFA)
• Cloud access security broker (CASB)
• Secrets management

5.1 - Build and implement physical and logical infrastructure for cloud environment

• Hardware specific security configuration requirements (e.g., hardware security module (HSM) and Trusted Platform Module (TPM))
• Installation and configuration of management tools
• Virtual hardware specific security configuration requirements (e.g., network, storage, memory, central processing unit (CPU), Hypervisor type 1 and 2)
• Installation of guest operating system (OS) virtualization toolsets

5.2 - Operate and maintain physical and logical infrastructure for cloud environment

• Access controls for local and remote access (e.g., Remote Desktop Protocol (RDP), secure terminal access, Secure Shell (SSH), console-based access mechanisms, jumpboxes, virtual client)
• Secure network configuration (e.g., virtual local area networks (VLAN), Transport Layer Security (TLS), Dynamic Host Configuration Protocol (DHCP), Domain Name System Security Extensions (DNSSEC), virtual private network (VPN))
• Network security controls (e.g., firewalls, intrusion detection systems (IDS), intrusion prevention systems (IPS), honeypots, vulnerability assessments, network security groups, bastion host)
• Operating system (OS) hardening through the application of baselines, monitoring and remediation (e.g., Windows, Linux, VMware)
• Patch management
• Infrastructure as Code (IaC) strategy
• Availability of clustered hosts (e.g., distributed resource scheduling, dynamic optimization, storage clusters, maintenance mode, high availability (HA))
• Availability of guest operating system (OS)
• Performance and capacity monitoring (e.g., network, compute, storage, response time)
• Hardware monitoring (e.g., disk, central processing unit (CPU), fan speed, temperature)
• Configuration of host and guest operating system (OS) backup and restore functions
• Management plane (e.g., scheduling, orchestration, maintenance)

5.3 - Implement operational controls and standards (e.g., Information Technology Infrastructure Library (ITIL), International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 20000-1)

• Change management
• Continuity management
• Information security management
• Continual service improvement management
• Incident management
• Problem management
• Release management
• Deployment management
• Configuration management
• Service level management
• Availability management
• Capacity management

5.4 - Support digital forensics

• Forensic data collection methodologies
• Evidence management
• Collect, acquire, and preserve digital evidence

5.5 - Manage communication with relevant parties

• Vendors
• Customers
• Partners
• Regulators
• Other stakeholders

5.6 - Manage security operations

• Security operations center (SOC)
• Intelligent monitoring of security controls (e.g., firewalls, intrusion detection systems (IDS), intrusion prevention systems (IPS), honeypots, network security groups, artificial intelligence (AI))
• Log capture and analysis (e.g., security information and event management (SIEM), log management)
• Incident management
• Vulnerability assessments

6.1 - Articulate legal requirements and unique risks within the cloud environment

• Conflicting international legislation
• Evaluation of legal risks specific to cloud computing
• Legal framework and guidelines
• eDiscovery (e.g., International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 27050, Cloud Security Alliance (CSA) Guidance)
• Forensics requirements

6.2 - Understand privacy issues

• Difference between contractual and regulated private data (e.g., protected health information (PHI), personally identifiable information (PII))
• Country-specific legislation related to private data (e.g., protected health information (PHI), personally identifiable information (PII))
• Jurisdictional differences in data privacy
• Standard privacy requirements (e.g., International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 27018, Generally Accepted Privacy Principles (GAPP), General Data Protection Regulation (GDPR))
• Privacy Impact Assessments (PIA)

6.3 - Understand audit process, methodologies, and required adaptations for a cloud environment

• Internal and external audit controls
• Impact of audit requirements
• Identify assurance challenges of virtualization and cloud
• Types of audit reports (e.g., Statement on Standards for Attestation Engagements (SSAE), Service Organization Control (SOC), International Standard on Assurance Engagements (ISAE))
• Restrictions of audit scope statements (e.g., Statement on Standards for Attestation Engagements (SSAE), International Standard on Assurance Engagements (ISAE))
• Gap analysis (e.g., control analysis, baselines)
• Audit planning
• Internal information security management system
• Internal information security controls system
• Policies (e.g., organizational, functional, cloud computing)
• Identification and involvement of relevant stakeholders
• Specialized compliance requirements for highly-regulated industries (e.g., North American Electric Reliability Corporation / Critical Infrastructure Protection (NERC / CIP), Health Insurance Portability and Accountability Act (HIPAA), Health Information Technology for Economic and Clinical Health (HITECH) Act, Payment Card Industry (PCI))
• Impact of distributed information technology (IT) model (e.g., diverse geographical locations and crossing over legal jurisdictions)

6.4 - Understand implications of cloud to enterprise risk management

• Assess providers risk management programs (e.g., controls, methodologies, policies, risk profile, risk appetite)
• Difference between data owner/controller vs. data custodian/processor
• Regulatory transparency requirements (e.g., breach notification, Sarbanes-Oxley (SOX), General Data Protection Regulation (GDPR))
• Risk treatment (i.e., avoid, mitigate, transfer, share, acceptance)
• Different risk frameworks
• Metrics for risk management
• Assessment of risk environment (e.g., service, vendor, infrastructure, business)

6.5 - Understand outsourcing and cloud contract design

• Business requirements (e.g., service-level agreement (SLA), master service agreement (MSA), statement of work (SOW))
• Vendor management (e.g., vendor assessments, vendor lock-in risks, vendor viability, escrow)
• Contract management (e.g., right to audit, metrics, definitions, termination, litigation, assurance, compliance, access to cloud/data, cyber risk insurance)
• Supply-chain management (e.g., International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 27036)

In the cloud architecture domain, AI integration begins with the evaluation of Cloud Service Provider (CSP) capabilities for hosting specialized AI workloads. Architects must now understand the shared responsibility model as it applies to AI-as-a-Service (AIaaS), specifically identifying where the provider’s responsibility for model infrastructure ends and the customer’s responsibility for model configuration and data begins. This includes designing for high-performance compute enclaves that can handle the massive throughput required for AI training while maintaining logical isolation.

Furthermore, this domain addresses the integration of AI into the cloud design process itself. The CCSP Exam Outline emphasizes the use of “Infrastructure as Code” (IaC) to deploy resilient AI environments and the application of cloud-native security design principles to mitigate the risks of model inversion and extraction. By building security into the foundation of the cloud-AI stack, CCSPs ensure that intelligent services are scalable, compliant and defensible from the moment they are provisioned.

Data is the most critical asset in the cloud-AI ecosystem, and Domain 2 focuses on its protection throughout the AI lifecycle. Integration efforts focus on the security of massive “Data Lakes” and training sets, implementing advanced discovery and classification tools that use AI to identify sensitive information before it enters an ML pipeline. The CCSP Exam Outline addresses the unique challenges of data sovereignty and jurisdictional risk when AI training data is processed across multiple cloud regions.

Additionally, this domain incorporates technical controls such as homomorphic encryption and differential privacy to protect data during the inference phase. Practitioners manage the “Data Remanence” risks associated with ephemeral storage used by AI nodes, ensuring that sensitive training residuals are completely purged. This ensures that the use of cloud-based AI does not inadvertently lead to data leakage or a violation of global privacy regulations.

This domain focuses on the hardened infrastructure required to run AI safely in the cloud. The CCSP Exam Outline integrates AI by addressing the security of the virtualization and containerization layers that host ML models. CCSPs are tasked with implementing specialized micro-segmentation to isolate AI training clusters and utilizing cloud-native hardware security modules (HSMs) to protect the cryptographic keys used for model signing and data encryption.

Moreover, we consider the role of AI in physical and logical infrastructure defense. This includes the administration of cloud-based DDoS protection and Web Application Firewalls (WAF) that leverage machine learning to detect and block sophisticated “Low and Slow” attacks targeting AI endpoints. By securing the underlying cloud platform, we ensure that the compute resources powering AI remain available and resilient against adversarial manipulation.

As cloud applications increasingly leverage AI APIs, Domain 4 addresses the security of these integrations. The CCSP Exam Outline incorporates the secure development lifecycle (SDLC) for AI-driven cloud apps, focusing on the risks of insecure API calls and the potential for “Inference Attacks” at the application layer. Practitioners are able to implement robust input validation and rate limiting to defend against prompt injection and automated resource exhaustion targeting AI-based features.

This domain also covers the security of the software supply chain, specifically regarding the inclusion of third-party ML libraries and pre-trained models. CCSPs perform security testing on AI-augmented applications, ensuring that automated “hallucinations” or logic flaws do not introduce new vulnerabilities into the production environment. This ensures that cloud applications remain secure even as they become more autonomous and complex.

In the cloud SOC, AI serves as a vital tool for managing the sheer scale of cloud-generated logs. Integration within Domain 5 focuses on using AI and ML for advanced threat hunting and event correlation across multi-cloud environments. Candidates are aware that cloud-native SIEM/SOAR platforms can automate the response to common cloud threats, allowing human analysts to focus on high-complexity AI-driven attacks.

Operationally, this domain also addresses the “Continuous Monitoring” of AI performance to detect security-related “Model Drift.” CCSPs are responsible for maintaining the operational baseline of AI services, ensuring that any deviation in model output is investigated as a potential security incident. This proactive approach ensures that cloud-based AI systems remain reliable and secure throughout their operational lifespan.

The final domain addresses the complex regulatory landscape surrounding cloud-based AI. The CCSP Exam Outline integrates AI by focusing on the legal implications of automated data processing and the requirements for “Explainability” under frameworks like the GDPR or the EU AI Act. CCSPs understand how to conduct Cloud Data Life Cycle audits that specifically account for how AI models process and store data across international borders.

Risk management in this domain involves assessing the “Vendor Risk” of AI service providers, ensuring that their security controls and ethical guidelines align with organizational standards. We also cover the role of eDiscovery and digital forensics in the cloud when AI is involved, ensuring that practitioners can effectively investigate incidents and provide auditable evidence of compliance in a rapidly evolving legal environment.