Selection and management of artificial intelligence applications to secure and analyze large databases #254021

Upon completion of this course, participants will be able to:

• Evaluate AI Solutions for Data Ecosystems: Critically assess AI and machine learning applications for database security (threat detection, access control) and analytics (pattern recognition, predictive modeling).

• Architect AI-Enhanced Data Security: Design and implement a layered security strategy for large databases using AI for real-time anomaly detection, data classification, encryption management, and compliance auditing.

• Manage AI-Driven Data Analysis: Oversee the use of AI tools to automate data cleaning, perform complex analysis on massive datasets, generate insights, and create data visualizations.

• Integrate AI Tools into Existing Infrastructure: Develop a strategy for integrating new AI applications with existing database management systems (DBMS), data warehouses, lakes, and security tools.

• Govern and Administer AI Data Systems: Establish robust governance frameworks to address data quality, model bias, ethical AI use, regulatory compliance (GDPR, CCPA, HIPAA), and performance monitoring.

• Calculate ROI and Build a Business Case: Articulate the value and return on investment of implementing AI for database security and analytics to executive leadership.

This course is designed for professionals responsible for the stewardship, security, and utilization of large data assets:

• Database Administrators (DBAs) & Data Architects

• Chief Information Security Officers (CISOs) & IT Security Managers

• Data Scientists & Senior Data Analysts

• Chief Data Officers (CDOs) & Data Governance Managers

• IT Directors & Heads of Infrastructure

• Cloud Architects & DevOps Engineers managing data platforms

• Compliance & Risk Officers focused on data protection

• Business Intelligence (BI) & Analytics Managers

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of AI for Large-Scale Data Management

• AM: The Modern Data Challenge: Scale, Security, and Insight

  • The evolution of data platforms: from RDBMS to Data Lakes and Lakehouses.

  • Why traditional tools fail: The case for AI in database security and analytics.

  • Key AI Concepts: Machine Learning, Deep Learning, and NLP in the context of data operations.

• PM: The AI Application Landscape for Databases

  • Overview of AI tools for: Data Security, Anomaly Detection, Automated Classification, Query Optimization, and Advanced Analytics.

  • Understanding deployment models: Cloud-native vs. on-premise AI solutions.

  • Workshop: Assessing your organization’s current data ecosystem and identifying key pain points.

Day 2: Selecting and Implementing AI for Database Security

• AM: AI-Powered Threat Detection and Prevention

  • Evaluating AI solutions for real-time monitoring: detecting unauthorized access, SQL injection attempts, and anomalous data exfiltration patterns.

  • AI for Data Loss Prevention (DLP): Classifying sensitive data and enforcing policies dynamically.

  • Implementing AI-driven database activity monitoring (DAM).

• PM: Proactive Security and Access Management

  • AI for vulnerability management: Identifying misconfigurations and patch priorities.

  • Behavioral analytics for user and entity behavior analytics (UEBA) to detect insider threats.

  • Vendor Evaluation Exercise: Creating a scorecard to compare AI-powered database security tools.

Day 3: Selecting and Implementing AI for Data Analytics

• AM: AI-Driven Data Processing and Insight Generation

  • Selecting tools for automated data cleaning, enrichment, and transformation at scale.

  • AI for pattern recognition and predictive analytics on massive datasets.

  • Using Natural Language Processing (NLP) to analyze unstructured data within databases.

• PM: Optimizing Performance and Accessibility

  • AI for automated query optimization and performance tuning.

  • Implementing Natural Language Query (NLQ) interfaces for business user self-service.

  • Generating automated insights and reports: From descriptive to prescriptive analytics.

  • Workshop: Using a cloud-based AI tool to run an analysis on a sample large dataset.

Day 4: Integration, Governance, and Management

• AM: Architecting for Integration and Performance

  • Strategies for integrating AI applications with existing DBMS (e.g., SQL Server, Oracle), cloud data platforms (BigQuery, Redshift, Snowflake), and ETL pipelines.

  • Managing the infrastructure: compute resources, storage, and networking for AI workloads.

• PM: Ethics, Governance, and Compliance

  • Ensuring AI model fairness and mitigating bias in data analysis.

  • Data governance for AI: Lineage, quality, and cataloging.

  • Navigating compliance: Demonstrating how AI tools help meet GDPR, HIPAA, and other regulatory requirements for data auditing and protection.

  • Group Discussion: Developing a governance charter for AI use on corporate data.

Day 5: Strategy, ROI, and Capstone

• AM: Building the Business Case and Managing the Project

  • Calculating ROI: Quantifying risk reduction, efficiency gains (e.g., DBA time saved), and improved business outcomes from analytics.

  • Developing a phased implementation and change management plan.

  • Upskilling the team: New roles and skills required to manage AI data applications.

• PM: Capstone Project and Future Trends

  • Capstone Exercise: Teams are given a scenario involving a data breach and an analytics request. They must select a portfolio of AI tools to address both, justifying their selection based on security, analytical power, and integration.

  • Future Trends: Generative AI for synthetic data generation and code automation, the future of autonomous databases, and privacy-preserving AI (e.g., federated learning).

  • Course Wrap-Up: Final presentations and development of a personal strategic action plan.