AKYLADE AI Security Foundation (A/AISF) Full Course & Exam

Explore the fundamentals of artificial intelligence, including machine learning, natural language processing, computer vision, deep learning, and generative AI, with a focus on accuracy, fairness, and security via case study.

Discover how artificial intelligence lets machines think, learn, and recognize speech. Explore machine learning, natural language processing, and computer vision, and the three AI types: narrow, general, and super.

Explore the fundamentals of machine learning, a core component of artificial intelligence that learns from data, identifies patterns, and makes predictions to improve accuracy.

Natural language processing enables machines to understand, interpret, and generate human language, powering virtual assistants, speech recognition, and automated translation with transformer models like BERT and GPT.

Explore computer vision and how deep learning with CNNs enables machines to interpret images, detect objects, and support applications from facial recognition to autonomous driving and medical imaging.

Deep learning uses multi-layered neural networks to learn representations from raw data, enabling image recognition, natural language processing, and autonomous systems with human-like performance.

Discover generative AI, from GANs and transformers to GPT, and learn how GenAI creates original text, images, video, and human-like outputs that empower automation.

Analyze how GPT-4 and computer vision automate content creation and enhance customer interactions, with examples from Copy.ai, Shopify chatbots, and Amazon Go’s cashier-less retail.

Discover the fundamentals of machine learning and deep learning, including neural networks, supervised and reinforcement learning, data preprocessing, and a PayPal fraud detection case study.

Explore machine learning techniques that let systems learn from data and improve accuracy, using six types: supervised, unsupervised, reinforcement, semi-supervised, self-supervised, and transfer learning.

Explore machine learning algorithms that let AI learn from data, make predictions, and automate decisions using regression, classification, and clustering, with methods like regression, SVR, decision trees, SVM, and k-means.

Explore how neural networks, the backbone of deep learning, use input, hidden, and output neurons and layers to recognize patterns and make predictions across AI tasks.

Explore neural network architectures, including dense, convolutional, and recurrent layers, and how CNNs and RNNs process images and sequences with weights, biases, activation functions like ReLU, sigmoid, tanh, and softmax.

Learn how neural networks learn from data through forward propagation, the loss function, backward propagation, and optimization. Improve accuracy by adjusting weights with SGD, Adam, and RMSprop.

Data preprocessing cleans, normalizes, and transforms raw data to improve model accuracy and training efficiency while reducing bias and enabling effective feature extraction.

Explore data preprocessing methods to prime machine learning models, including data augmentation, data cleaning, data normalization, and data reduction, to improve accuracy and training efficiency.

Explore the applications of deep learning across image, video, and speech recognition, fraud detection, and algorithmic trading, powered by cnn, rnn, and lstm models in security, healthcare, finance, and e-commerce.

Examine PayPal's AI-powered fraud detection, where deep learning, data preprocessing, and feature engineering identify real-time fraud with high accuracy and fewer false positives.

Explore the AI lifecycle from plan and design through deploy and monitor, emphasizing data quality, model development, governance, and ethical considerations for aligned, responsible AI.

Phase one of the AI lifecycle focuses on plan and design, aligning objectives with business goals, defining scope, and integrating fairness, transparency, accountability, privacy, and governance.

Collect and process data to build reliable ai models by ensuring data quality, compliant collection methods, and privacy and retention policies. Transform, normalize, and engineer features to improve model performance.

Build and use the model in phase three of the ai lifecycle by selecting the right algorithm, training and evaluating performance, mitigating bias and ensuring transparency, and deploying with monitoring.

Phase four verify and validate confirms the AI model is correctly implemented, performs reliably, and meets ethical and regulatory standards through verification, validation, bias checks, and security assessments.

Deploy and use phase converts validated AI models into real-world systems with scalable infrastructure, cloud platforms, seamless integration, regulatory compliance, and ongoing bias mitigation through continuous monitoring.

Operate and monitor AI systems after deployment to maintain accuracy, fairness, and security by tracking performance, detecting drift, and preventing bias creep.

Explore phase seven of the ai lifecycle, use or impacted by, and examine real-world impact, user interaction, stakeholder effects, societal considerations, and continuous feedback for accountability and refinement.

Explore the four key ai system dimensions, including application context, data and input, ai model, and task and output, and how they influence ethics, regulation, data quality, and business outcomes.

Define objectives and preprocess data to build robust AI models; train, evaluate, and tune while preventing overfitting, use feature engineering, then deploy with continuous monitoring and updates.

Explore how HealthFirst built an agile AI model with ClosedLoop.ai to predict hospital readmissions and chronic health risks, integrate EHRs and claims data, and deploy AI-driven insights into value-based care.

Identify AI actors across the AI system lifecycle—from design to governance—and learn how collaboration, TEVV, and human factors, plus Google DeepMind case study, shape ethical, reliable AI.

Explore ai design as a core practice that defines objectives, architecture, and ethical parameters while ensuring secure, scalable, and trustworthy data processing and governance.

Develop robust ai systems by transforming designs into functioning software with engineers and security specialists, ensuring secure data, rigorous testing, and protection against data poisoning and reverse engineering.

Secure AI deployment transitions models from development to production in cloud environments with encryption and identity management, using zero-trust, continuous monitoring, and automated rollback.

Track AI system performance through continuous monitoring to detect unauthorized access, anomalies, and drift, and implement security updates to keep models accurate and secure.

Explore the roles of quality assurance teams, independent evaluators, and regulatory bodies in TEVV, and learn how stress testing, adversarial testing, and real-world simulations validate AI accuracy, fairness, and security.

Identify the human factors driving AI security by examining how end users, administrators, and developers influence AI systems, and learn to minimize errors, biases, and security risks.

Domain experts bring specialized industry knowledge to AI, ensuring regulatory and ethical compliance, with HIPAA privacy protection and guardrails against hallucinations across healthcare, law, finance, insurance, and defense.

Policy analysts, risk managers, and ethicists assess AI impact to safeguard society, ethics, and security. They evaluate privacy risks, bias, and job displacement, guiding responsible, compliant deployment before go-live.

Identify the AI actors in procurement and how to select tools, data, and services that meet security, privacy, ethics, and operational standards.

Explore AI governance and oversight, the roles of governments, boards, and compliance officers, and how frameworks enforce privacy protections such as HIPAA, audits, and bias prevention in high-risk sectors.

Explore how AI actors beyond developers shape AI development, deployment, security, and regulation. Assess roles of third-party entities, end users, advocacy groups, standards bodies, researchers, environmental groups, and civil society.

Analyze a case study of Google's DeepMind and the NHS that examines how AI deployment affects patient data privacy, consent, governance, and early diagnosis of acute kidney injury (AKI).

Assess AI maturity levels and develop strategies for AI implementation that align with business goals, emphasizing cross-functional collaboration, governance, centers of excellence, roadmaps, and continuous evaluation.

Foster cross-functional collaboration across architecture, security, compliance, legal, operations, and business strategy to design responsible, secure AI solutions. Early involvement builds trust, uncovers blind spots, and accelerates delivery.

Define roles for enterprise architects, security teams, devops, and business representatives to build secure, scalable AI systems, ensuring alignment with business goals throughout the lifecycle.

Develop a strong organizational culture to support AI by enabling open collaboration across data scientists, security, DevOps, and leadership. Foster curiosity, responsible experimentation, and ethics: fairness, transparency, accountability, and privacy.

Align AI strategies with the organization's mission and business objectives to turn AI into a strategic asset, guided by AI vision and mission statements, governance, leadership, and a business case.

Build a centralized AI center of excellence to govern, train, and iteratively deploy AI, aligning with organizational goals while ensuring ethics, privacy, fairness, and transparency.

Develop a structured AI roadmap that aligns initiatives with organizational goals. Bridge planning to execution through a phased rollout with pilots, checkpoints, and short-term and long-term goals.

Monitor and evaluate AI initiatives with continuous metrics and KPIs, regular reviews, and user feedback to ensure they stay effective, secure, aligned with business goals, and scalable over time.

Explore AI maturity levels and how Gartner's five stages and the Cisco AI Readiness Index guide assessment of technology, processes, and people to align AI with goals.

Assess your organization's current AI capabilities, data readiness, infrastructure, and workforce to align with business goals before scaling, ensuring secure data, leadership support, and a clear budget.

Engage executives, technical staff, and end users through interviews and surveys to shape an inclusive AI strategy, building trust and readiness for secure, value-driven adoption.

Identify capability gaps in people, processes, and technology to scale and mature AI initiatives. Improve talent, governance, and infrastructure to ensure secure, compliant, and efficient AI growth.

Explore how Microsoft advanced its ai maturity by deploying an ai powered virtual assistant in customer support, closing capability gaps, and upskilling staff to boost response times and satisfaction.

Explain AI risk management principles and key terms. Demonstrate risk measurement and mitigation strategies across the AI lifecycle, including risk tolerance, appetite, and ethical, technical, and operational risks.

Define risk tolerance in AI systems and explain how GDPR and the EU AI Act, plus internal policies, set the upper risk boundary while balancing ethics, privacy, and innovation.

Assess how much risk an organization is willing to pursue to achieve strategic goals, balancing risk appetite with risk tolerance to guide AI innovation, safety, and governance.

Identify inherent, residual, and control risks in AI systems to build a secure AI security strategy spanning design through post-deployment monitoring.

Explore risk treatment strategies: acceptance, avoidance, mitigation, and transference, aligned with an organization's risk tolerance, with examples like monitoring, encryption, audits, and cyber insurance.

Explore risk measurement in AI security, quantifying, evaluating, and tracking risks across data collection, training, deployment, and maintenance to ensure secure, reliable systems amid third-party, emergent, and real-world challenges.

Explore AI risk management tools, including NIST AI RMF, ISO 27005, ISO 31000, OCTAVE, FAIR, vulnerability assessments, threat modeling, penetration testing, IBM's AI Fairness 360, and Google's What-If tools.

The Amazon case study shows how an AI hiring tool learned bias from historical data, highlighting the need for proactive risk management, fairness testing, and accountability.

Master AI risk assessments by identifying, analyzing, evaluating, and mitigating risks; document and report findings; and monitor and review to keep AI systems secure, ethical, and aligned with objectives.

Identify AI risks early by defining objectives and scope, exploring adversarial attacks and model drift, and applying threat modeling with STRIDE and LINDDUN to safeguard privacy and compliance.

Identify and analyze AI system risk scenarios by evaluating likelihood and impact, prioritizing mitigation across model, data, and infrastructure, including ethical, operational, and regulatory dimensions.

Risk evaluation prioritizes identified risks by impact and likelihood, aligns decisions with risk tolerance and regulatory obligations, and validates them through penetration testing, red teaming, and model audits.

Develop and deploy targeted risk mitigation strategies for AI systems by identifying sources of risk, implementing controls, and continuously monitoring to reduce bias, adversarial threats, and privacy concerns.

Document and report AI risk management by detailing the risk assessment, mitigation choices, and security controls, then tailor stakeholder communications to ensure transparency, accountability, and regulatory compliance.

Monitor and review the AI risk management lifecycle to continuously track system performance, detect anomalies and model drift, and regularly update risk assessments to stay aligned with regulations and ethics.

Examine a real-world Apple Card case study to understand AI-driven risk management, bias, privacy, and ongoing audits that strengthen fairness and data protection in consumer finance.

Master AI governance through policies, frameworks, and accountability to ensure ethical, compliant, and responsible AI across the organization, including IBM Watson for Oncology case study.

Integrate AI risk management into organizational governance by building cross-functional collaboration, leadership engagement, and cultural transformation to align AI with business objectives, comply with laws, and earn stakeholder trust.

Learn how documentation strengthens ai risk management within governance by detailing frameworks, policies, practices, and processes that ensure transparency, accountability, and regulatory compliance.

Learn how development guidelines embed ai risk management across the lifecycle through risk by design, data quality, transparency, security, and ethics to align with regulatory requirements and values.

Explore emerging AI risk management guidance and standards, including ISO 27090, 27091, 31000, 42001, and the EU AI Act, to ensure secure, ethical, and compliant AI.

Coordinate board, CEO, CRO, CISO, CDO, CLO, AI ethics officers, legal and compliance, internal audit, project managers, and business unit leaders to ensure secure, ethical, compliant AI aligned with strategy.

Examine the IBM Watson for Oncology case to explore AI governance, transparency, and risk management in healthcare, highlighting training data bias, explainability, and ongoing validation.

Explore the trustworthiness of AI systems, covering reliability, safety, security, transparency, and fairness, and learn to balance these characteristics for context-based risk reduction through objectives 3.1 and 3.2.

Explore what makes AI systems valid and reliable, focusing on accuracy, robustness, and generalizability to ensure trustworthy, consistent performance across real-world conditions.

Discover what makes AI safe and trustworthy, from data collection to deployment, and how proactive planning, robust testing, impact assessments, and fail-safes prevent harm.

Guard AI security and resilience by enforcing encryption, authentication, access controls, secure data handling, and real-time monitoring to maintain trustworthy operation through redundancies, backups, automated error handling, and failover.

Develop accountable and transparent AI systems to build trust, enable ethical decisions, and ensure responsible oversight with clear roles, explainable reasoning, and compliant data use.

Explore why explainable and interpretable AI builds trust by revealing decision logic in plain language, enabling fairness, transparency, and recourse in real-world outcomes.

Explore how privacy-enabled AI systems protect personal data through strategies like data minimization, anonymization, encryption, access controls, audit logs, and federated learning across the full AI lifecycle.

Explore how to define fairness in AI and mitigate harmful bias through data auditing, diverse sampling, and ongoing evaluation of models and outcomes across population groups.

Balance the characteristics of trustworthy ai by prioritizing validity, reliability, safety, security, transparency, fairness, and privacy. Design choices adjust to context-specific needs across science, healthcare, finance, government, and hiring.

Examine the Uber self-driving car incident in Tempe, Arizona, to illustrate failures in real-time perception and the consequences for safety, reliability, accountability, and transparency.