Course Introduction
Artificial intelligence is increasingly being used to support programme evaluation, impact assessment, evidence synthesis, beneficiary analysis, automated reporting, and development performance monitoring. Although these systems can improve analytical efficiency and expand access to evidence, they can also introduce vulnerabilities that undermine accuracy, fairness, reliability, privacy, and institutional accountability. The Adversarial Testing and Red Teaming of AI-Based Evaluation Systems Training Course equips participants with systematic methods for identifying, testing, documenting, and mitigating weaknesses in AI-supported evaluation workflows before those weaknesses affect research findings, programme decisions, or beneficiary outcomes.
AI-based evaluation systems may combine large language models, machine learning classifiers, predictive analytics, retrieval-augmented generation, automated scoring, semantic search, and data-processing pipelines. Each component introduces potential failure modes, including manipulated inputs, prompt injection, misleading source documents, data leakage, biased classifications, fabricated evidence, and inconsistent outputs. These weaknesses may be difficult to detect through conventional functional testing because an AI system can appear to operate correctly while producing unreliable results under unusual or adversarial conditions. Participants will learn how to examine AI systems from an adversarial perspective, identify realistic threat scenarios, and determine whether a system remains dependable when exposed to ambiguous, deceptive, incomplete, or deliberately manipulated information.
Effective red teaming requires a structured methodology that connects system understanding, threat modeling, attack simulation, evidence collection, impact assessment, and remediation. Participants will explore how to define the evaluation system's intended purpose, map its data flows and dependencies, identify critical assets, establish realistic attacker assumptions, and prioritize scenarios according to likelihood and potential harm. They will examine adversarial testing techniques for language models, retrieval systems, classification models, automated evidence extraction, and AI-assisted scoring tools. Practical exercises will focus on controlled test environments, synthetic evaluation documents, approved datasets, and documented test cases, ensuring that participants develop technical competence while respecting data protection, authorization, and research ethics requirements.
The course places particular emphasis on the risks that arise when AI systems are used to evaluate development interventions, public policies, institutional performance, or beneficiary outcomes. Adversarial inputs may distort reported findings, conceal negative results, exaggerate programme effectiveness, misclassify vulnerable populations, or introduce unsupported conclusions into evaluation reports. Participants will investigate how source manipulation, contextual ambiguity, selection bias, distribution shifts, and conflicting evidence can influence automated analysis. They will learn to test whether a system preserves source attribution, distinguishes evidence from interpretation, handles uncertainty appropriately, and resists attempts to influence its conclusions through misleading content or carefully crafted instructions.
Technical testing alone is insufficient if findings are not translated into meaningful risk reduction. The course therefore examines how to evaluate the severity of discovered weaknesses, measure exploitability and downstream consequences, document reproducible evidence, and recommend proportionate corrective actions. Participants will develop skills in designing adversarial test suites, defining performance thresholds, evaluating robustness, measuring false positives and false negatives, testing refusal and escalation behavior, and validating remediation through regression testing. They will also explore governance considerations involving human oversight, incident response, audit trails, model updates, supplier accountability, privacy, fairness, and independent assurance. The programme emphasizes that red teaming should complement, rather than replace, standard software testing, model validation, security assessment, and responsible AI governance.
By completing the Adversarial Testing and Red Teaming of AI-Based Evaluation Systems Training Course, participants will be able to establish repeatable red teaming processes tailored to AI-supported evaluation environments. They will gain practical capabilities in threat modeling, adversarial scenario development, prompt-injection testing, evidence-integrity assessment, robustness evaluation, privacy-risk testing, failure analysis, and remediation verification. Through guided laboratories, realistic case studies, and an integrated capstone exercise, participants will produce a red team assessment report containing prioritized findings, supporting evidence, risk ratings, corrective recommendations, and a retesting plan. The course prepares professionals to strengthen the reliability of AI evaluation systems while protecting evidence quality, affected communities, institutional trust, and the integrity of development decisions.
Duration
10 days
Who Should Attend
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AI governance officers responsible for testing the reliability, security, and accountability of AI-supported evaluation systems.
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Monitoring, evaluation, and learning specialists using AI for evidence extraction, outcome analysis, programme assessment, or automated reporting.
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AI red teamers and security professionals assessing the robustness of language models, machine learning systems, and AI-enabled applications.
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Data scientists and machine learning engineers developing models for evaluation scoring, prediction, classification, and evidence analysis.
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AI application developers building retrieval-augmented generation systems, semantic search platforms, automated research assistants, and evaluation tools.
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Internal auditors and assurance specialists reviewing AI controls, testing evidence, system reliability, and the effectiveness of risk mitigation measures.
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Cybersecurity professionals responsible for threat modeling, adversarial testing, application security, AI security, and incident preparedness.
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Evaluation researchers investigating bias, measurement error, evidence manipulation, model limitations, and the validity of AI-generated findings.
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Public policy analysts and development programme managers overseeing AI-assisted decisions that may affect services, resource allocation, or beneficiary outcomes.
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Data protection and privacy specialists assessing information leakage, confidential document exposure, and risks involving sensitive evaluation records.
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Responsible AI and ethics specialists examining fairness, transparency, human oversight, and the consequences of AI failure in development settings.
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Quality assurance professionals establishing test cases, performance thresholds, regression testing, and defect-management procedures for AI systems.
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Technology risk managers assessing third-party AI services, model dependencies, supplier controls, and operational vulnerabilities.
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Consultants supporting AI system audits, model assurance, evaluation technology reviews, and responsible AI implementation.
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Professionals seeking practical expertise in adversarial testing, AI robustness, red teaming, and evidence-integrity assurance.
Course Objectives
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Develop a structured red teaming methodology for AI-based evaluation systems covering scoping, threat modeling, adversarial testing, impact assessment, remediation, and verification.
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Identify vulnerabilities across language models, retrieval systems, automated evidence extraction, classification tools, predictive models, and AI-supported evaluation workflows.
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Construct realistic threat models that identify critical assets, trust boundaries, attack surfaces, adversary capabilities, likely failure modes, and potential consequences.
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Design controlled adversarial test cases involving prompt injection, misleading documents, conflicting evidence, ambiguous instructions, data poisoning scenarios, and manipulated inputs.
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Evaluate AI system robustness, reliability, and analytical integrity using repeatable test protocols, appropriate performance metrics, and clearly defined acceptance criteria.
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Test whether AI evaluation systems preserve source attribution, distinguish evidence from interpretation, handle uncertainty, and avoid unsupported or fabricated findings.
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Assess privacy and security risks involving sensitive evaluation documents, confidential beneficiary information, retrieval leakage, unauthorized access, and inappropriate data disclosure.
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Identify and measure fairness-related weaknesses, including inconsistent performance across languages, demographic groups, geographic contexts, and different types of evaluation evidence.
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Apply risk-based severity assessment to prioritize vulnerabilities according to exploitability, likelihood, potential harm, system exposure, and downstream decision consequences.
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Produce reproducible red team findings supported by test inputs, expected behavior, observed outputs, evidence records, impact analysis, and remediation recommendations.
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Design mitigation and regression testing procedures that verify whether corrective changes reduce identified weaknesses without introducing new failures.
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Develop an organizational red teaming roadmap integrating authorization, governance, testing frequency, independent review, incident escalation, supplier engagement, and continuous assurance.
Comprehensive Course Outline
Module 1: Foundations of AI Red Teaming for Evaluation Systems
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Examine the purpose of adversarial testing and distinguish red teaming from functional testing, conventional cybersecurity testing, model validation, and independent AI assurance.
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Identify AI applications in evaluation research, including evidence retrieval, document classification, automated scoring, outcome prediction, and generative reporting.
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Analyze how AI failures can affect evidence quality, evaluation validity, programme recommendations, beneficiary treatment, and institutional decision-making.
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Establish principles for authorized, proportionate, documented, and reproducible red teaming using controlled environments and clearly defined testing boundaries.
Module 2: AI System Scoping, Architecture, and Attack Surface Mapping
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Map system components, including user interfaces, prompts, models, document repositories, retrieval services, APIs, databases, external tools, and reporting interfaces.
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Identify trust boundaries, data flows, privileged operations, external dependencies, human review points, and interfaces through which adversarial inputs may enter.
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Define critical assets such as evaluation evidence, confidential records, model instructions, scoring rules, analytical outputs, and decision-support recommendations.
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Develop a system scope document defining authorized test environments, permitted techniques, prohibited activities, test accounts, data restrictions, and stopping conditions.
Module 3: Threat Modeling and Adversarial Scenario Design
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Identify relevant threat actors, including malicious users, compromised suppliers, untrusted document contributors, external attackers, and accidental sources of harmful input.
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Apply threat modeling techniques to connect system assets, attacker objectives, vulnerabilities, attack paths, potential impacts, and existing controls.
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Develop adversarial scenarios involving evidence manipulation, instruction conflicts, data exposure, biased scoring, misleading sources, and unauthorized system behavior.
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Prioritize scenarios according to likelihood, exploitability, operational exposure, affected populations, and potential consequences for evaluation integrity.
Module 4: Prompt Injection and Instruction-Handling Robustness
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Examine direct and indirect prompt injection risks in AI systems that process user instructions, retrieved documents, uploaded files, web content, and evaluation reports.
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Design controlled tests to determine whether untrusted source content can override intended task instructions or influence the system's behavior inappropriately.
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Assess instruction hierarchy, source isolation, structured extraction constraints, tool permissions, and other defensive measures under realistic adversarial conditions.
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Document failures using safe, reproducible test cases and recommend mitigations that separate untrusted evidence content from privileged instructions and actions.
Module 5: Adversarial Testing of Retrieval-Augmented Evaluation Systems
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Evaluate retrieval pipelines for vulnerabilities involving poisoned or misleading documents, manipulated metadata, irrelevant passages, source confusion, and retrieval of unauthorized records.
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Test whether the system correctly attributes findings to their original sources and distinguishes document content from instructions embedded within retrieved passages.
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Assess retrieval quality under conflicting evidence, strategically worded documents, incomplete context, and adversarially constructed passages.
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Verify access controls, source filtering, citation integrity, and evidence-grounding requirements across retrieval, synthesis, and reporting stages.
Module 6: Evidence Integrity, Hallucination, and Analytical Reliability
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Test whether AI systems generate unsupported claims, invent quotations, misrepresent source findings, confuse correlation with causation, or omit material limitations.
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Develop adversarial cases containing contradictory findings, misleading summaries, incomplete datasets, ambiguous indicators, and inconsistent reporting periods.
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Assess the system's ability to communicate uncertainty, acknowledge missing evidence, distinguish explicit findings from inference, and abstain when information is insufficient.
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Establish verification procedures that compare generated outputs against original source material and record the nature and significance of analytical errors.
Module 7: Adversarial Robustness of Classification and Predictive Models
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Evaluate how classification, scoring, and prediction systems respond to noisy inputs, missing fields, distribution shifts, ambiguous labels, and plausible but misleading records.
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Test model stability under controlled perturbations that preserve legitimate meaning while revealing sensitivity to irrelevant or misleading input changes.
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Examine threshold behavior, calibration, false-positive rates, false-negative rates, and changes in performance across relevant data subgroups.
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Assess the consequences of model errors for beneficiary segmentation, programme risk scoring, outcome classification, and automated evaluation recommendations.
Module 8: Fairness, Bias, and Cross-Context Stress Testing
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Design tests that examine whether system performance differs across relevant demographic groups, geographic contexts, languages, programme types, and evidence formats.
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Assess how unbalanced datasets, historical inequities, proxy variables, inconsistent terminology, and missing representation can influence AI-generated evaluation findings.
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Apply subgroup performance analysis and carefully controlled counterfactual comparisons to identify potentially unjustified differences in system behavior.
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Develop mitigation recommendations involving improved data coverage, threshold review, contextual validation, human oversight, and ongoing fairness monitoring.
Module 9: Privacy, Confidentiality, and Data Leakage Testing
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Identify privacy risks associated with sensitive evaluation documents, beneficiary records, personal information, confidential programme data, and restricted institutional reports.
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Test authorized scenarios for unintended disclosure through model responses, retrieval results, generated summaries, logs, caches, and system integrations.
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Examine whether access controls and document-level permissions remain effective throughout ingestion, indexing, retrieval, synthesis, and reporting workflows.
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Develop privacy-risk findings and corrective recommendations covering data minimization, access restrictions, secure processing, retention, logging, and incident response.
Module 10: Tool Use, APIs, and AI Agent Security
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Assess risks associated with AI systems that invoke APIs, access databases, execute workflow actions, or interact with external tools and services.
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Examine permission boundaries, authentication, authorization, input validation, output validation, and separation between analytical recommendations and consequential actions.
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Test whether untrusted inputs can trigger unintended tool calls, unauthorized data access, inappropriate workflow changes, or violations of predefined operational constraints.
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Recommend controls involving least privilege, explicit authorization, action confirmation, tool allowlists, monitoring, and safe failure behavior.
Module 11: Test Harnesses, Metrics, and Automated Red Team Workflows
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Develop structured test suites containing attack scenarios, expected behavior, test inputs, severity criteria, execution instructions, and reproducible evidence records.
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Apply appropriate metrics for robustness, extraction accuracy, source attribution, refusal behavior, privacy leakage, fairness, and overall task performance.
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Use automation to run repeatable tests across model versions, prompts, retrieval configurations, datasets, and system releases while controlling test conditions.
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Establish test coverage measures, regression suites, exception handling, result comparison, and reporting procedures for continuous AI assurance.
Module 12: Risk Scoring, Finding Validation, and Impact Assessment
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Evaluate identified vulnerabilities using documented criteria for likelihood, exploitability, exposure, affected stakeholders, potential harm, and existing safeguards.
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Distinguish theoretical weaknesses from demonstrated failures and assess whether a finding is reproducible under realistic system conditions.
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Analyze downstream consequences for evidence validity, programme decisions, institutional compliance, beneficiary welfare, and organizational reputation.
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Prioritize findings into actionable remediation categories with accountable owners, risk acceptance requirements, target dates, and verification procedures.
Module 13: Mitigation Engineering and Defensive Control Validation
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Develop targeted mitigations for prompt injection, unreliable retrieval, source attribution failures, unsupported generation, privacy leakage, and inappropriate automated actions.
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Evaluate defensive approaches such as input validation, instruction isolation, access controls, source allowlists, constrained outputs, human approval, and independent verification.
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Test mitigation effectiveness against the original adversarial scenarios and additional cases designed to identify unintended side effects.
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Establish evidence-based acceptance criteria that confirm whether the residual risk is within the organization's documented tolerance.
Module 14: Governance, Authorization, and Responsible Red Team Operations
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Establish written authorization, scope boundaries, testing permissions, data handling requirements, confidentiality expectations, and escalation procedures before testing begins.
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Integrate red teaming with AI governance committees, cybersecurity, privacy, internal audit, programme assurance, safeguarding, and risk management functions.
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Define responsibilities for test approval, execution, independent review, finding ownership, remediation acceptance, and final closure.
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Develop procedures for sensitive findings, responsible disclosure, incident escalation, evidence preservation, and communication with suppliers or affected stakeholders.
Module 15: Continuous Monitoring, Model Changes, and Regression Assurance
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Establish triggers for retesting after model upgrades, prompt changes, new data sources, retrieval index updates, supplier changes, and expanded system use.
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Monitor production indicators for unusual outputs, shifts in error patterns, changing subgroup performance, security incidents, and unexpected system behavior.
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Maintain versioned test cases, model configurations, datasets, risk records, remediation histories, and approval evidence to support reproducibility.
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Design periodic assurance reviews that combine automated regression tests, targeted expert assessment, user feedback, incident analysis, and control effectiveness evaluation.
Module 16: Integrated AI Red Teaming Capstone and Assurance Reporting
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Conduct an authorized red team assessment of a representative AI-based evaluation system using a predefined scope, threat model, and prioritized test plan.
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Execute controlled tests covering instruction handling, retrieval integrity, evidence attribution, robustness, privacy, and selected fairness or tool-use risks.
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Prepare a structured report documenting demonstrated findings, supporting evidence, severity ratings, potential impacts, corrective actions, and unresolved uncertainties.
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Present a retesting and governance roadmap that connects remediation ownership, deadlines, regression testing, independent verification, and ongoing assurance responsibilities.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
