Cambridge
Academy
In London

Introduction

AI is increasingly used to strengthen database security by detecting anomalies, identifying suspicious access patterns, reducing alert fatigue, and accelerating investigation and response. This practical program equips database leaders and security teams with AI-enabled methods to monitor database activity, hunt threats, manage access risk, and integrate AI-driven detection into governance and incident response—while ensuring privacy, explainability, and audit readiness.

Course Objectives

By the end of this course, participants will be able to:

·        Understand where AI adds value in database security and where human oversight is essential

·        Design AI-enabled monitoring for database activity, privilege abuse, and data exfiltration signals

·        Apply AI techniques for anomaly detection, event correlation, and alert optimization

·        Strengthen access risk analytics: privileged access monitoring, entitlement reviews, and SoD controls

·        Integrate AI-driven detection with incident response, evidence handling, and remediation

·        Establish governance, privacy controls, and validation practices for trustworthy AI security use

Target Audience

This course is designed for:

·        Database leads, DBA managers, and senior DBAs

·        Security operations (SOC) and threat hunting teams supporting data platforms

·        IAM/PAM professionals responsible for access governance

·        Data governance, privacy, and compliance professionals overseeing sensitive data environments

·        IT risk and internal audit professionals involved in security assurance

Course Outlines

Day 1: AI Foundations for Database Security & Readiness Assessment

·        Database threat landscape: insider risk, misconfigurations, privilege abuse, and exfiltration paths

·        AI in security: detection, correlation, prediction, and automation concepts

·        Data needed for AI security: audit logs, query telemetry, identity events, and change history

·        Limitations and risks: false positives/negatives, drift, and explainability challenges

·        Activity: Database security monitoring maturity check + AI use-case backlog and prioritization

Day 2: AI-Enabled Database Activity Monitoring (DAM) & Telemetry Design

·        What to monitor: logins, privilege changes, schema changes, sensitive table access, high-risk queries

·        Telemetry standards: tagging, user identity context, and service mapping concepts

·        Baselining “normal”: seasonality, batch jobs, maintenance windows, and trusted processes

·        Alert design: thresholds vs. anomaly models, confidence scoring, and routing

·        Workshop: Build a DAM monitoring blueprint (signals + alerts + triage workflow + escalation rules)

Day 3: Anomaly Detection, Correlation & Threat Hunting

·        Anomaly detection concepts: outliers, behavior deviations, and sequence anomalies

·        Event correlation: linking DB events with IAM, network, and application signals

·        Threat hunting playbooks: hypotheses, search patterns, and evidence collection

·        Reducing noise: suppression rules, whitelisting discipline, and feedback loops for tuning

·        Practical activity: Threat hunt simulation using sample events (identify, confirm, and document findings) 

Day 4: Access Risk Analytics & Privileged Activity Oversight

·        Access risk in databases: excessive privileges, dormant accounts, shared accounts, and SoD conflicts

·        AI for entitlement reviews: clustering roles, identifying outliers, and right-sizing access

·        Privileged access monitoring: session analytics, command/query risk scoring, and JIT concepts

·        Integrating controls: access approvals, periodic reviews, and exception handling

·        Case study: Investigating suspected privilege abuse and designing preventative controls

Day 5: Governance, Privacy, Validation & Incident Response Integration

·        Responsible AI governance: roles, approvals, accountability, and escalation paths

·        Privacy and confidentiality: minimizing sensitive data exposure in logs and AI workflows

·        Validation and monitoring: accuracy tracking, drift detection, and audit trails

·        Incident response integration: playbooks, containment actions, communications, and post-incident learning