Skip to main content
Image coming soon

GEN0501 Mastering Application Recovery in the Age of AI

$199.00
Adding to cart… The item has been added

The Executive Diagnostic and Governance Toolkit

Mastering Application Recovery in the Age of AI

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing cyber recovery is no longer about restoring files, it’s about reviving entire applications after AI breaks them. This means traditional backup strategies fail when AI agents reconfigure or delete entire cloud applications, not just data. Dreeze restores full application states post-attack or AI error, while Daylight embeds AI agents into security operations, proving that recovery must now include dependency mapping and behavioral rollback. Before your next performance review, assume any AI tool in your stack could trigger a cascading failure that existing DR plans won’t catch. The immediate question: Test your current disaster recovery plan against a simulated AI agent mistake that deletes a core cloud service configuration.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
Your disaster recovery plan assumes data loss. AI breaks applications in ways it can’t fix.

The situation this is built for

Cyber recovery has evolved. When AI agents reconfigure or delete entire cloud applications, traditional backup strategies fail. You’re left scrambling to restore not just data, but configurations, dependencies, and behavioral states. A single AI mistake can cascade through services, and if your recovery plan doesn’t account for full application states, you’ll face extended downtime and compliance exposure. The tools you trusted no longer suffice.

Who this is for

IT, operations, compliance, or service management lead responsible for application recovery and business continuity

Who this is not for

Individual contributors not accountable for end-to-end recovery outcomes or team leaders outside of enterprise IT and cloud operations

What you walk away with

  • Validate recovery beyond data restoration
  • Map critical service dependencies accurately
  • Run realistic AI error simulations
  • Align recovery outcomes with audit requirements
  • Lead recovery planning that anticipates AI-driven configuration drift

How this maps to your situation

  • Current recovery plans fail under AI-induced configuration loss
  • Teams lack tools to validate full application state recovery
  • Compliance requirements are not embedded in recovery workflows
  • Leadership does not fully understand recovery limitations

Before vs. after

Before
You rely on outdated backup strategies that don’t account for AI-driven configuration loss, leaving critical applications vulnerable to extended downtime and compliance failures.
After
You lead a recovery function that validates full application states, maps dependencies, runs AI error simulations, and produces audit-ready evidence after incidents.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 3 hours per module, designed for busy professionals to complete at their own pace over 8 to 12 weeks.

If nothing changes
Without updating your recovery approach, a single AI mistake could cascade into prolonged outages, regulatory penalties, and loss of stakeholder trust. Your current plan won’t detect or reverse configuration deletions, leaving systems unrecoverable when you need them most.

How this compares to the alternatives

Unlike generic disaster recovery training, this course focuses specifically on application recovery after AI-induced failures. It does not cover vendor tools, funding trends, or startup innovations. Instead, it delivers actionable frameworks, templates, and decision pathways for leaders accountable for recovery outcomes.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Reframing Recovery for AI-Driven Outages
Shift from data-centric to application-state recovery thinking, aligning with modern AI risks.
12 chapters in this module
  1. Understanding how AI agents alter cloud configurations
  2. Identifying gaps in traditional backup approaches
  3. Defining full application state recovery
  4. Recognizing the limits of point-in-time snapshots
  5. Assessing recovery objectives in an AI environment
  6. Evaluating the role of configuration drift
  7. Documenting service dependencies in cloud ecosystems
  8. Measuring recovery completeness beyond data
  9. Aligning recovery goals with compliance mandates
  10. Tracking behavioral changes in automated systems
  11. Establishing recovery success criteria for AI events
  12. Reviewing real-world AI-induced failure scenarios
Module 2. Mapping Application Dependencies Accurately
Build precise dependency maps to prevent cascading failures during recovery.
12 chapters in this module
  1. Inventorying cloud services with configuration metadata
  2. Mapping data flow between AI-integrated systems
  3. Identifying hidden dependencies in serverless environments
  4. Documenting API relationships for recovery sequencing
  5. Tracking configuration inheritance across environments
  6. Using dependency graphs in outage planning
  7. Validating dependency maps with runtime telemetry
  8. Updating dependency documentation after deployments
  9. Classifying dependencies by recovery priority
  10. Integrating dependency data into incident playbooks
  11. Automating dependency discovery without vendor tools
  12. Conducting peer reviews of dependency accuracy
Module 3. Designing for Behavioral Rollback
Recover not just configurations, but expected system behaviors after AI interference.
12 chapters in this module
  1. Defining normal system behavior for recovery baselines
  2. Capturing pre-incident behavioral patterns
  3. Using logs to reconstruct service interactions
  4. Differentiating between data and behavior loss
  5. Rebuilding state machines after AI reconfiguration
  6. Validating service responses post-recovery
  7. Implementing canary checks for behavioral accuracy
  8. Documenting expected output from recovered services
  9. Using synthetic transactions to verify functionality
  10. Reconciling state across distributed components
  11. Establishing behavioral rollback success metrics
  12. Auditing recovered systems for compliance alignment
Module 4. Testing Recovery Against AI Scenarios
Simulate AI-driven configuration deletions to validate recovery readiness.
12 chapters in this module
  1. Creating realistic AI error simulation scenarios
  2. Selecting high-risk services for test cases
  3. Designing test plans that mimic agent actions
  4. Running tabletop exercises with operations teams
  5. Injecting configuration drift into staging environments
  6. Measuring time to detect AI-induced changes
  7. Validating rollback procedures under pressure
  8. Involving compliance staff in test design
  9. Documenting recovery decision points
  10. Analyzing test results for process gaps
  11. Updating playbooks based on test findings
  12. Scheduling recurring AI failure simulations
Module 5. Configuring Immutable Recovery Environments
Ensure recovery environments resist AI tampering and maintain integrity.
12 chapters in this module
  1. Isolating recovery infrastructure from production
  2. Implementing write-once, read-many storage policies
  3. Enforcing access controls for recovery systems
  4. Using air-gapped environments for critical recovery
  5. Validating recovery environment integrity regularly
  6. Preventing AI agents from accessing backup stores
  7. Documenting recovery environment architecture
  8. Integrating hardware security modules where applicable
  9. Establishing change freeze protocols
  10. Auditing recovery environment configurations
  11. Testing recovery from isolated snapshots
  12. Maintaining recovery environment documentation
Module 6. Integrating Compliance into Recovery Design
Build recovery workflows that meet audit and regulatory requirements.
12 chapters in this module
  1. Mapping recovery steps to compliance controls
  2. Documenting recovery actions for auditors
  3. Including timestamped logs in recovery artifacts
  4. Proving data consistency across recovery events
  5. Aligning recovery timelines with SLA requirements
  6. Demonstrating separation of duties in recovery
  7. Capturing evidence during rollback operations
  8. Using templates to standardize compliance reporting
  9. Reviewing recovery logs for completeness
  10. Preparing for third-party audit scrutiny
  11. Updating compliance documentation after tests
  12. Integrating recovery evidence into control frameworks
Module 7. Orchestrating Recovery Across Teams
Coordinate IT, security, and compliance teams during AI-driven outages.
12 chapters in this module
  1. Defining roles in AI-induced incident response
  2. Establishing communication protocols during recovery
  3. Creating cross-functional recovery playbooks
  4. Conducting joint training for shared understanding
  5. Using war rooms for coordinated recovery
  6. Assigning decision authority during outages
  7. Documenting handoffs between teams
  8. Integrating service management workflows
  9. Managing stakeholder updates under pressure
  10. Resolving conflicts in recovery priorities
  11. Reviewing team performance after incidents
  12. Building shared accountability for outcomes
Module 8. Automating Recovery Validation
Use scripts and checks to verify recovery completeness without manual effort.
12 chapters in this module
  1. Writing scripts to validate service availability
  2. Automating configuration comparison checks
  3. Using checksums to confirm state integrity
  4. Triggering validation after rollback events
  5. Integrating validation into CI/CD pipelines
  6. Alerting on validation failures
  7. Building dashboard views for recovery status
  8. Scheduling regular recovery dry runs
  9. Using synthetic data to test recovery paths
  10. Logging validation results for audits
  11. Updating validation rules after system changes
  12. Ensuring validation scripts are version controlled
Module 9. Managing Configuration Drift Proactively
Detect and control configuration changes before they impact recovery.
12 chapters in this module
  1. Monitoring for unauthorized configuration changes
  2. Establishing baselines for approved states
  3. Using drift detection tools without vendor lock-in
  4. Alerting on high-risk configuration modifications
  5. Requiring approvals for critical changes
  6. Documenting change justifications
  7. Reviewing configuration history regularly
  8. Integrating change logs into recovery planning
  9. Preventing AI agents from bypassing controls
  10. Enforcing infrastructure as code standards
  11. Auditing configuration management processes
  12. Updating baselines after approved changes
Module 10. Leading Recovery Strategy Discussions
Facilitate conversations that align leadership on recovery readiness.
12 chapters in this module
  1. Presenting recovery risks to executive stakeholders
  2. Translating technical gaps into business impact
  3. Prioritizing recovery investments based on risk
  4. Facilitating cross-departmental recovery planning
  5. Building consensus on recovery time objectives
  6. Communicating recovery test results effectively
  7. Managing expectations during recovery incidents
  8. Incorporating feedback from post-mortems
  9. Aligning recovery goals with business continuity
  10. Documenting strategic decisions for accountability
  11. Preparing for board-level recovery inquiries
  12. Leading recovery maturity assessments
Module 11. Documenting Recovery for Audit Readiness
Create defensible, evidence-based recovery documentation for compliance teams.
12 chapters in this module
  1. Structuring recovery runbooks for clarity
  2. Including version control in documentation
  3. Using standardized templates across services
  4. Embedding compliance requirements in playbooks
  5. Maintaining revision history for audits
  6. Storing documentation in tamper-evident formats
  7. Linking recovery steps to control objectives
  8. Ensuring accessibility during outages
  9. Training staff on documentation updates
  10. Validating documentation during tests
  11. Archiving recovery records securely
  12. Updating documentation after system changes
Module 12. Sustaining Recovery Excellence Over Time
Maintain and improve recovery capabilities as AI systems evolve.
12 chapters in this module
  1. Scheduling recurring recovery capability reviews
  2. Updating recovery plans after system changes
  3. Tracking key recovery performance metrics
  4. Incorporating lessons from incident reviews
  5. Benchmarking against industry standards
  6. Adjusting recovery strategies for new AI tools
  7. Conducting annual recovery readiness assessments
  8. Maintaining team skills through drills
  9. Sharing recovery improvements across departments
  10. Documenting recovery evolution over time
  11. Aligning recovery with emerging regulations
  12. Planning for next-generation AI risks

Frequently asked

Who is this course designed for?
IT, operations, compliance, or service management leads who own application recovery and business continuity outcomes.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover specific vendor tools or products?
No. The course focuses on the work of application recovery, not on endorsing or teaching any specific vendor’s solution.
Will I learn how to test my current disaster recovery plan?
Yes. You will build and run simulations that test recovery against AI-driven configuration deletions and reconfigurations.
Is there a practical component to apply the learning?
Yes. Every module includes downloadable templates and examples, and you receive a hand-built implementation playbook to guide real-world application.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per module, designed for busy professionals to complete at their own pace over 8 to 12 weeks..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
Thousands of organisations have bought from The Art of Service since 2000.
!