Cloud + AI Security Operations Workshop by Tonex
Cloud-hosted artificial intelligence (AI) workflows expand the scope of security operations beyond traditional access logs and infrastructure alerts. This workshop examines cloud identity, data pipelines, workload telemetry, model service access, and human oversight in operational detection and response. Participants analyze how prompt injection, sensitive data exposure, overprivileged service identities, and insecure integrations can generate observable security events. The discussion distinguishes model output from verified evidence, and emphasizes secure handling of incident context sent to AI-enabled assistants. Through realistic investigation cases, security professionals develop procedures for triage, protected automation, auditability, and measured deployment of AI-supported analytical capabilities.
Cybersecurity operations must protect both cloud resources and AI application data flows. Reliable cybersecurity decisions require independently verifiable evidence, controlled privileges, and explicit human approval of consequential response actions.
The workshop uses a practical training approach that includes exercises, real-world case studies, and examples of processes and documentation used in cloud and AI security operations projects, including model access inventories, detection use cases, investigation playbooks, and approval records.
Learning Objectives
- Map cloud identities, model services, data stores, and security monitoring boundaries.
- Assess threats involving prompt injection, exposed data, and privileged integrations.
- Correlate cloud audit events with application and AI service activity.
- Define safe human oversight for AI-assisted alert triage and response.
- Protect sensitive investigation materials and validate generated analytical findings.
- Develop accountable operational procedures and evidence-based security metrics.
- Strengthen cybersecurity detection while limiting AI-related operational and privacy risks.
Audience
- Cybersecurity Professionals
- Cloud Security Operations Analysts
- AI Application Security Engineers
- Security Detection Engineers
- Cloud Incident Response Leads
- Security Automation Architects
- Data Protection and Privacy Officers
Course Modules
Module 1: Cloud and AI Security Boundaries
- Map cloud accounts, workloads, identity providers, and AI services
- Identify trust boundaries among prompts, retrieval data, and tools
- Define responsibility for hosted and externally provided model services
- Catalog sensitive datasets exposed through AI application workflows
- Review cloud audit coverage for privileged AI integrations
- Document business ownership and incident escalation relationships
Module 2: AI Enabled Threat and Risk Analysis
- Assess prompt injection and untrusted retrieval content risks
- Identify exposed credentials within automation connectors and service accounts
- Examine data leakage risks in prompts and stored responses
- Review unsafe tool permissions and indirect action pathways
- Distinguish incorrect model output from verified compromise evidence
- Prioritize threats by data sensitivity and operational consequences
Module 3: Cloud Telemetry and Detection Engineering
- Correlate access logs with AI endpoint and application events
- Define alerts for unusual model service permissions changes
- Monitor unexpected data export from retrieval and storage systems
- Review application gateways, identity events, and policy denials
- Create investigation baselines for known legitimate usage patterns
- Track coverage gaps across managed cloud and AI services
Module 4: Safe AI Assisted Security Triage
- Define permitted incident data supplied to analytical assistants
- Apply review requirements before accepting generated event summaries
- Validate suggested indicators against primary source audit records
- Restrict automated actions using scoped service identities
- Record analyst decisions and the evidence supporting escalation
- Address confidential information retention and output handling rules
Module 5: Incident Response and Access Containment
- Investigate suspected credential misuse in AI application integrations
- Assess prompt-driven unauthorized actions against independent audit evidence
- Coordinate service identity revocation with application operations teams
- Preserve relevant request metadata under approved privacy safeguards
- Plan controlled recovery of model connectors and data access
- Document post-incident findings and residual integration exposure
Module 6: Operational Governance and Assurance
- Maintain inventories of AI connectors and approved data flows
- Define detection performance and human review quality indicators
- Review vendor responsibilities and event logging availability
- Establish change approval for new AI-enabled security features
- Prepare audit records for access and response authorization
- Update playbooks as services and model capabilities evolve
Advance evidence-based cloud defense with Cloud + AI Security Operations Workshop by Tonex. Develop secure, accountable practices for monitoring AI-enabled services and cloud infrastructure.