ISSEP Certification Exam Outline Summary

1.1 Apply systems security engineering fundamentals

• Systems security engineering trust concepts and hierarchies
• Relationships between systems and security engineering processes
• Structural security design principles (e.g., National Institute of Standards and Technology (NIST) engineering framework, International Organization for Standardization (IS0) 27001)

1.2 Execute systems security engineering processes (e.g., hardware, software, data)

• Organizational security authorities (e.g., internal, external)
• System security governance and compliance (e.g., laws, regulations, standards)
• Design concepts (e.g., open, proprietary, modular)

1.3 Integrate with system development methodology

• Security tasks and activities
• Security requirements verification throughout the process
• Assurance methods (e.g., software, hardware, virtual, cloud)
• Models (e.g., System Development Life Cycle (SDLC), International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 24641:2023, Model based systems engineering)

1.4 Perform technical management 

• Project management processes participation
• Configuration management (CM) processes
• Information management processes
• Measurement processes
• Quality assurance (QA) processes
• Security process automation solution evaluations

1.5 Participate in the technology procurement management

• Security requirements for acquisitions
• Selection process
• Supply chain risk management (SCRM)
• Review security related contractual deliverables (e.g., hardware, software, services, documentation)

1.6 Resource Analysis (e.g., Cost estimation, personnel costs, probabilities and statistics (Monte Carlo))

• Cost estimation
• Personnel costs
• Probabilities and statistics (Monte Carlo method, mean time between failures (MTBF), Maximum Tolerable Downtime (MTD), mean time to failure (MTTF), mean time to repair (MTTR), mean time to recovery (MTTR))

2.1 Apply security risk management principles 

• Security risk management alignment with enterprise risk management
• Risk management integration throughout the lifecycle

2.2 Manage risk to system 

• Establish risk context 
• Identify system security risks (e.g., threats, events, vulnerabilities, impact) 
• Perform inherent risk analysis 
• Perform risk evaluation 
• Monitoring and evaluate changes to risk posture (e.g., residual, changed, new) 
• Documenting risk posture (e.g., findings, decisions)

2.3 Manage risk to operations

• Establish risk context 
• Identify system security risks (e.g., threats, events, vulnerabilities, impact) 
• Perform inherent risk analysis 
• Perform risk evaluation 
• Monitoring and evaluate changes to risk posture (e.g., residual, changed, new) 
• Documenting risk posture (e.g., findings, decisions)

3.1 Analyze organizational and operational environment

• Capture stakeholder requirements
• Identify roles and responsibilities
• Identify relevant constraints and assumptions
• Prepare security validation plan

3.2 Apply system security principles

• Resiliency methods (e.g., redundancy, component diversity/disparity)
• Layered security concepts (e.g., defense-in-depth, Zero Trust, secure-by-default)
• Fail-safe defaults (e.g., fail open, fail secure, fail closed)
• Single points of failure
• Least privilege
• Economy of mechanism
• Separation of interfaces, functions, services, and roles
• Automation (e.g., threat response, SecDevOps, emerging technologies)
• Software assurance
• Data security

3.3 Develop system requirements

• Develop system security context
• Identify functions within the system and security concept of operations
• Document system security requirements baseline
• Analyze system security requirements

3.4 Create system security design

• Develop functional analysis and allocation
• Develop system security design components
• Maintain traceability between specified design and system requirements
• Perform trade-off studies
• Validate design

4.1 Implement and integrate security solutions 

• Perform system security implementation and integration
• Support on-going system security activities (e.g., Continuous Integration and Continuous Delivery (CI/CD), DevSecOps) 

4.2 Verify successful implementation

• Develop security test plans
• Support system security verification
• Review and update risk analysis
• Document stakeholder acceptance in system implementation

5.1 Develop secure operations plan

• Identify roles, responsibilities, and requirements for system security personnel conducting operations
• Specify requirements for security related event reporting

5.2 Support secure operations 

• Design continuous monitoring functionality (e.g., personnel, processes, technology)
• Support the incident response process
• Develop secure maintenance procedures

5.3 Participate in change management

• Participate in change reviews
• Assess change impact
• Perform verification and validation of changes
• Update risk assessment documentation

5.4 Participate in the disposal process 

• Identify disposal security requirements
• Develop secure disposal plan
• Develop decommissioning and disposal procedures
• Audit results of the decommissioning and disposal process
• Implement data retention policies

In the foundational domain of the ISSEP, AI integration focuses on the mathematical and theoretical validation of machine learning models within a system’s security architecture. Security engineers are now tasked with applying formal methods to AI components, ensuring that “Algorithmic Integrity” is maintained throughout the system lifecycle. This involves integrating AI-specific threat modeling—such as identifying vulnerabilities to adversarial evasion—directly into the initial design requirements to ensure that the system’s baseline is resilient against non-deterministic threats.

Furthermore, this domain addresses the incorporation of AI into the “Security Design Principles.” Engineers must now account for the unique computational and data-handling requirements of neural networks, ensuring that the integration of an AI sub-system does not violate established security enclaves or trust boundaries. By treating the ML model as a high-value engineering asset, ISSEPs ensure that the foundation of the system is built to withstand both traditional and AI-augmented attack vectors.

Risk management for the security engineer has evolved to include the probabilistic risks inherent in AI. Within this domain, the ISSEP Exam Outline integrates methodologies for assessing “Model Robustness” and the potential for logic drift in production environments. Practitioners utilize AI-driven assessment tools to perform continuous, automated vulnerability scanning of complex system interdependencies, shifting from point-in-time audits to a real-time understanding of the system’s risk posture.

Moreover, the ISSEP Exam Outline focuses on the engineering of “Explainable AI” as a core risk-mitigation strategy. By designing systems that provide transparent, auditable pathways for their decisions, engineers can provide the necessary evidence for security authorizations and certifications. This integration ensures that even the most complex deep-learning components can meet the rigorous documentation and verification standards required for high-assurance environments.

This domain focuses on the “Build” phase, where AI is integrated into the secure systems development lifecycle (SSDLC). Security engineers are tasked with designing secure data ingestion pipelines that prevent “Data Poisoning” during the training phase. The integration emphasizes the use of hardware-rooted trust, such as Trusted Execution Environments (TEE), to protect AI model weights and inference logic from unauthorized access or tampering at the physical layer.

Additionally, the ISSEP Exam Outline addresses the secure integration of AI APIs and third-party ML libraries. Engineers must evaluate the “provenance” of pre-trained models, ensuring that the software supply chain for the system’s intelligence is as secure as the code itself. By embedding AI security requirements into the functional design specifications, ISSEPs ensure that the finished system is not only intelligent but also architecturally sound and defensible.

Verification and Validation are critical in engineering, and this domain includes the testing of AI outputs against established security policies. Integration involves developing “Adversarial Testing” protocols where engineers attempt to trick or bypass AI-driven controls to verify their resilience. This ensures that the system’s automated responses are consistent, predictable and do not introduce “hallucinations” that could compromise mission success.

By utilizing machine learning to parse massive amounts of testing data and system logs, engineers can more effectively identify edge cases and logic flaws that traditional testing might miss. This dual-sided integration ensures that as systems become more complex, the engineering tools used to validate them remain equally sophisticated and effective.

The final domain addresses the long-term sustainability of AI-integrated systems. Engineers are tasked with designing “Continuous Monitoring” frameworks that specifically track model performance and integrity over time. This includes establishing automated triggers for model retraining or system rollback if “Concept Drift” or a security compromise is detected. This ensures that the system remains within its authorized security parameters throughout its entire operational lifespan.

Maintenance also involves the secure patching and updating of AI models in the field. ISSEPs must design secure delivery mechanisms for large-scale model updates, ensuring that the integrity of the “intelligence” is verified at every step of the update process. By integrating AI into the maintenance cycle, the ISSEP Exam Outline ensures that the system remains resilient against evolving threats and that the “human-in-the-loop” remains empowered to oversee and control autonomous system functions.