AI-Assisted Software Development
Enterprise-safe SDLC practices
Outcome: Use AI in the SDLC without introducing IP, privacy, or security risk.
SDLC-aligned practices for code generation, review, and secure use of AI tools.
Implementation Outcome
This course covers safe use of AI across the software development lifecycle, focusing on IP protection, secure coding practices, and alignment with existing SDLC controls.
- SDLC AI usage standard (by phase)
- IP and confidential data protection checklist
- Secure code review and verification workflow
Controls & Evidence
- Designed for records retention, version control, and documented review cadence
- Supports internal control alignment and defensible oversight practices
- Produces an audit-reviewable evidence set suitable for internal audit request workflows
Data handling: No submission of sensitive or proprietary data is required to complete the program.
Risk Exposure
Lack of defensible policies or controls can result in significant, unmitigated enterprise risk exposure. This program addresses the risk areas most relevant to this capability.
- IP leakage through prompts, snippets, or uploads
- Introduction of insecure code patterns or dependencies
- Undocumented AI use in regulated development environments
- Insufficient code review rigor for generated output
Deliverables
SDLC AI Usage Standard
Phase-by-phase rules for design, coding, testing, and deployment.
IP & Confidentiality Guardrails
What cannot be submitted, how to sanitize inputs, and how to document.
Secure Coding & Review Workflow
Verification steps for AI-generated code and dependency risk.
Evidence Pack for Oversight
Artifacts suitable for engineering leadership and audit review.
Governance Lifecycle Integration
- Baseline: Establish policy-aligned use patterns and minimum control expectations across affected teams.
- Oversight: Assign accountable owners, decision rights, and escalation paths for AI-assisted activities.
- Monitoring: Define review cadence, metrics, and control checks aligned to operational reality.
- Documentation: Maintain version-controlled artifacts and evidence suitable for records retention and review.
- Audit Review: Enable internal audit and leadership review with traceable controls, decisions, and evidence.
Buyer Questions
Does this require sharing confidential data with the provider?
No. The program is designed for policy, controls, and safe-use practices. Participants can complete the program without submitting sensitive or proprietary data.
Who should attend?
Developers, DevOps, QA
What evidence is produced for audit review?
Version-controlled artifacts (policy templates, oversight workbook outputs, and control-aligned documentation) suitable for internal audit requests and governance reviews.
How is it deployed?
On-demand delivery with enterprise licensing options. LMS and SSO integration can be included in rollout scoping.
How are artifacts maintained over time?
Artifacts are designed for version control and periodic review. Organizations can align updates to internal change management and records retention requirements.
Request Enterprise Pricing
For rollout scoping (seat counts, deployment model, LMS/SSO integration, and licensing options), request enterprise pricing and deployment scope.