Certified ISO27090 Lead AI Security Manager

 

Introduction to Brit Certifications and Assessments UK (BCAA)

 

Brit Certifications and Assessments UK (BCAA) is a specialized certification body based in the United Kingdom. It acts as a "quality seal" for businesses and professionals, particularly those working in the high-stakes worlds of IT, cybersecurity, and data privacy.

Think of BCAA like a driving school and a licensing authority combined: they don’t just teach you how to drive (Training); they also test you to make sure you’re safe on the road (Assessment) and give you a license that proves it to others (Certification).

 

The "Read-Act-Certify-Engage" Framework

 

BCAA uses a specific four-step model to help people master new skills. This ensures that a certification isn't just a piece of paper, but a true reflection of ability.
1. Read: You start by learning the theory and understanding the rules.
2. Act: You apply that knowledge through practical exercises and real-world scenarios.
3. Certify: You take an exam to prove you have mastered the subject.
4. Engage: After passing, you stay involved through webinars and group discussions to keep your skills sharp.

 

Agenda

 

Module 1: Foundations of Artificial Intelligence & Machine Learning Architecture

 

• 1.1 Core ML Workflow: Understanding the pipeline from data ingestion to model inference and deployment.
• 1.2 Neural Networks & Algorithms: Deep dive into deep learning, supervised/unsupervised learning, and Large Language Models (LLMs).
• 1.3 AI vs. Traditional Software: Identifying why standard IT architecture models fail to capture AI logic gaps.
• 1.4 The Shared Responsibility Model: Defining security ownership across data scientists, developers, and IT security teams.
• 1.5 Business Value & Risk Trade-offs: Balancing rapid AI deployment velocity with enterprise cybersecurity posture.
• 1.6 Executive Accountability: Translating technical AI metrics into board-level risk indicators.

 

Module 2: Introduction to ISO/IEC 27090 Standard Framework

 

• 2.1 Standard Architecture: Overview of ISO/IEC 27090 and its role as an extension to ISO/IEC 27001.
• 2.2 Scope and Applicability: Determining how the standard applies to distinct organizational AI use cases.
• 2.3 Core Terminology: Harmonizing vocabulary for AI cyber threats, attack vectors, and vulnerabilities.
• 2.4 Regulatory Alignment: Mapping ISO 27090 alongside the EU AI Act, NIST AI RMF, and ISO 42001.
• 2.5 Establishing the AI Security Mandate: Writing a corporate charter for an AI Security Management program.
• 2.6 Roles and Responsibilities: Defining the charter for a Lead AI Security Manager.

 

Module 3: AI Threat Taxonomy & Attack Surface Mapping

 

• 3.1 Comprehensive Attack Surface Analysis: Mapping vulnerabilities across data pipelines, model weights, and APIs.
• 3.2 Data Poisoning Vulnerabilities: Examining threats where malicious training inputs corrupt model integrity.
• 3.3 Evasion and Adversarial Attacks: Analyzing input perturbations designed to trick operational models.
• 3.4 Model Theft and Extraction: Mitigating risks of intellectual property cloning via systematic API querying.
• 3.5 Membership Inference & Privacy Leakage: Defending against attacks that extract training dataset records.
• 3.6 Prompt Injection & Jailbreaking: Securing generative AI and LLMs against malicious linguistic instructions.

 

Module 4: Integrating AI Security into the Information Security Management System (ISMS)

 

• 4.1 ISMS Extension Principles: Expanding ISO 27001 Annex A controls to cover machine learning environments.
• 4.2 Policy Customization: Drafting acceptable-use and security policies specific to internal AI initiatives.
• 4.3 Asset Management for AI: Cataloging proprietary datasets, pre-trained weights, and specialized hardware (GPUs/TPUs).
• 4.4 Access Control and Privileged Management: Restricting access to sensitive data labeling tools and model tuning parameters.
• 4.5 Cryptographic Protections: Securing data-in-transit, data-at-rest, and model parameters using encryption.
• 4.6 Internal Auditing Frameworks: Establishing recurring verification checks for AI security compliance.

Module 5: Secure AI Development Lifecycle (SecAI-DL)

 

• 5.1 Integrating Security into MLOps: Embedding continuous security validation inside automated pipelines.
• 5.2 Design-Phase Threat Modeling: Applying STRIDE or PASTA adaptations tailored for AI architectures.
• 5.3 Secure Data Curation & Handling: Vetting data collection, cleaning, and labeling processes for malicious tampering.
• 5.4 Code and Script Security: Mitigating vulnerabilities in Python-heavy data science codebases and libraries.
• 5.5 Model Version Control & Integrity: Utilizing cryptographic checksums to track immutable states of trained models.
• 5.6 Environment Hardening: Securing distributed computing environments (Kubernetes, cloud clusters) hosting training tasks.

 

Module 6: AI Supply Chain & Third-Party Vendor Risk Management

 

• 6.1 Third-Party Model Risks: Assessing commercial pre-trained foundation models and open-source hubs (e.g., Hugging Face).
• 6.2 Vendor Due Diligence: Designing technical questionnaires for external AI SaaS and API providers.
• 6.3 Dataset Provenance Tracking: Verifying legal and secure origins of externally purchased training data.
• 6.4 Open-Source Dependency Governance: Scanning open-source libraries for known vulnerabilities and backdoors.
• 6.5 Contractual Security SLAs: Enforcing compliance mandates, audit rights, and breach notification terms in vendor contracts.
• 6.6 Offboarding and Vendor Transition: Securely severing data-sharing pipelines when retiring a third-party AI tool.

 

Module 7: Data Governance, Privacy, and Confidentiality in AI

 

• 7.1 Privacy-Preserving Machine Learning (PPML): Overview of cryptographic and algorithmic safeguards for sensitive data.
• 7.2 Differential Privacy: Implementing statistical noise injection to prevent data re-identification.
• 7.3 Federated Learning Architectures: Training algorithms across decentralized devices without centralizing raw data.
• 7.4 Synthetic Data Generation: Utilizing artificially generated datasets to train models safely without privacy exposure.
• 7.5 Data Minimization in Training: Stripping Personally Identifiable Information (PII) before dataset compilation.
• 7.6 Cross-Border Data Transfer Controls: Managing geopolitical and regulatory restrictions on global AI training datasets.

 

Module 8: Adversarial Robustness and Model Hardening Techniques

 

• 8.1 Defensive Distillation: Training models to be resilient against adversarial perturbations.
• 8.2 Adversarial Training: Proactively injecting attacked samples into training cycles to build model immunity.
• 8.3 Input Sanitization Filters: Deploying guardrails and pre-processors to block adversarial inputs before inference.
• 8.4 Model Ensembling and Verification: Utilizing multiple distinct algorithms to cross-verify output accuracy.
• 8.5 Robustness Benchmarking: Standardizing metrics to test model stability against unexpected inputs.
• 8.6 Uncertainty Estimation: Programming models to signal low confidence when exposed to anomalous data.

 

Module 9: Real-Time Monitoring, Logging, and Observability

 

• 9.1 Shift from Static Logs to Behavior Analytics: Designing telemetry systems tailored for continuous machine learning inference. • 9.2 Model Drift and Data Decay Detection: Differentiating between natural operational drift and targeted data poisoning. • 9.3 Inference-Time Monitoring: Tracking latency, output anomalies, and unusual token consumption patterns in real time. • 9.4 Immutable Logging Infrastructure: Securing audit trails for data inputs and system outputs to ensure non-repudiation. • 9.5 Automated Alert Tuning: Establishing thresholds to minimize false positives in high-speed AI workflows. • 9.6 SIEM/SOAR Integration: Feeding AI operational metrics into broader enterprise security dashboards.

 

Module 10: AI Incident Detection, Response, and Forensics

 

• 10.1 Defining an AI Incident: Establishing criteria for what constitutes a breach in an automated machine learning system.
• 10.2 Custom Incident Playbooks: Drafting step-by-step containment procedures for model poisoning or prompt hijacking.
• 10.3 Rapid Isolation and Rollback: Safely reverting compromised models to previously known secure snapshots.
• 10.4 AI-Specific Digital Forensics: Preserving model states, logs, and random seeds for post-incident root-cause analysis.
• 10.5 Stakeholder Communication: Managing public, regulatory, and executive transparency during an AI security failure.
• 10.6 Post-Incident Learning Loops: Updating threat taxonomies and security controls based on forensic takeaways.

 

Module 11: Application Security for AI Interfaces and APIs

 

• 11.1 Securing AI Gateway and Middleware: Protecting public-facing endpoints that bridge user apps to core models.
• 11.2 OWASP Top 10 for LLMs / AI: Mapping web vulnerabilities to large-scale machine learning interfaces.
• 11.3 Rate Limiting and Abuse Prevention: Mitigating Denial of Service (DoS) attacks targeting expensive inference cycles.
• 11.4 Output Filtering and Content Safeguards: Blocking toxic, hazardous, or sensitive data leaks from generative responses.
• 11.5 Authentication & Session Security: Enforcing robust identity management for users querying enterprise AI tools.
• 11.6 Webhook and Callback Protection: Securing asynchronous communication pipelines between microservices and AI engines.

 

Module 12: Human-AI Interaction & Social Engineering Security

 

• 12.1 The Human Element in AI Security: Analyzing how users misplace trust in flawed algorithmic outputs.
• 12.2 Deepfake and Generative Phishing Defense: Training staff to spot AI-driven social engineering and impersonation attacks.
• 12.3 Over-Reliance and Automation Bias: Establishing governance protocols to prevent dangerous blind faith in AI decisions.
• 12.4 Red-Teaming AI Systems: Designing human-led and automated exercises to actively break enterprise models.
• 12.5 Secure Prompt Engineering Guidelines: Educating internal users on safe interaction parameters with enterprise LLMs.
• 12.6 Behavioral Monitoring: Tracking user interactions to spot insider threats attempting to exploit model workflows.

 

Module 13: Auditing, Assessment, and Compliance Management

 

• 13.1 ISO 27090 Compliance Audits: Structuring internal verification programs against the standard's criteria.
• 13.2 Technical Code & Model Auditing: Reviewing weights, hyperparameters, and code structures for hidden logic flaws.
• 13.3 Algorithmic Transparency & Explainability (XAI): Validating that model decisions can be audited and understood.
• 13.4 Managing Third-Party Assessments: Working with external certification bodies and auditors for formal accreditation.
• 13.5 Corrective Action Plans (CAP): Remediation lifecycle tracking for audit non-conformities and gaps.
• 13.6 Continuous Compliance Reporting: Generating automated executive dashboards for regulatory stakeholders.

 

Module 14: Emerging Trends, Quantum Threats, and Future Horizons

 

• 14.1 Quantum Computing Risks to AI Cryptography: Evaluating future cryptographic vulnerabilities in model exchange protocols.
• 14.2 Autonomous Agent Security: Securing multi-agent systems that interact and execute tasks independently.
• 14.3 Decentralized & Edge AI Security: Protecting AI running on constrained edge devices and Internet of Things (IoT) hardware.
• 14.4 Generative AI Evolution: Staying ahead of next-generation multi-modal threats (video, audio, text synthesis).
• 14.5 Automated Defensive AI: Leveraging adversarial machine learning to protect enterprise networks autonomously.
• 14.6 Future ISO Standard Revisions: Anticipating upcoming amendments to the global AI security framework ecosystem.

 

Module 15: Capstone Strategy — Designing an Enterprise AI Security Master Plan

 

• 15.1 Enterprise Context Analysis: Assessing an organization’s readiness and maturity profile for ISO 27090 implementation.
• 15.2 Budgeting and Resource Allocation: Structuring capital and operational expenditures for comprehensive AI security.
• 15.3 Change Management & Cultural Alignment: Overcoming friction between fast-moving data science squads and security teams.
• 15.4 Phased Implementation Roadmap: Building a multi-quarter milestones plan from initial assessment to audit-readiness.
• 15.5 Key Performance Indicators (KPIs): Measuring the success and maturity progression of the AI security program.
• 15.6 Boardroom Pitching: Structuring an executive presentation to secure leadership sponsorship and resource sign-off.

 

Module 16: Certification Preparation, Review, and Practical Examination

 

• 16.1 Comprehensive Syllabus Review: Synthesizing core technical controls, governance structures, and frameworks.
• 16.2 Sample Case Study Workshops: Dissecting complex real-world AI security breach scenarios.
• 16.3 Exam Blueprint & Strategy: Navigating question formats, analytical expectations, and time management.
• 16.4 Subjective Assessment Preparation: Training for BCAA UK structured evaluation formats and interview criteria.
• 16.5 Professional Research Article Guidance: Formatting and structuring the required AI security essay/submission.
• 16.6 Final Mock Examination: Administering a simulated test environment to ensure complete candidate readiness.

 

Exams

 

The Training is followed by Subjective exam for three hours.

 

Contact

 

BRIT CERTIFICATIONS AND ASSESSMENTS (UK),
128 City Road, London, EC1V 2NX,
United Kingdom enquiry@bcaa.uk