AI Governance Readiness Assessment
				

					We evaluate your current AI governance maturity and identify gaps across policy, risk management,
					security, privacy, vendor oversight, documentation, and operational control.
				
			

			


				
Assessment areas may include:

				


					

						
Current AI usage across departments
						
Existing AI-related policies and procedures
						
AI approval and review workflows
						
Security and privacy controls
						
Vendor risk management practices
					

					

						
Data handling rules
						
Employee AI usage expectations
						
Executive oversight and reporting
						
AI incident response readiness
						
Alignment with internal risk and compliance programs
					

				

			
		
	

	

		

			

				
AI Asset Inventory and Use Case Register
				

					Organizations cannot govern what they cannot see.
				
				

					We help identify and document AI systems, tools, vendors, models, workflows, and use cases across
					the business.
				
			

			


				
Inventory may include:

				


					

						
Internal AI tools
						
Public AI tools used by employees
						
SaaS platforms with embedded AI
						
Customer-facing AI systems
						
Internal copilots and assistants
					

					

						
AI-enabled analytics tools
						
AI used in development workflows
						
third-party AI vendors
						
LLM, RAG, agentic, and automation-based systems
						
Sensitive data flows connected to AI systems
					

				
				

					The result is a clearer view of where AI exists, who owns it, what data it touches, and what risk it
					introduces.
				

			
		
	

	

		

			

				
AI Acceptable Use Policy
				

					We create or refine practical AI acceptable use policies that employees can understand and follow.
				
			

			


				
Policy areas may include:

				


					

						
Approved and prohibited AI use
						
Sensitive data restrictions
						
Customer data handling
						
Confidential business information
						
Intellectual property protections
					

					

						
Employee accountability
						
Human review requirements
						
AI-generated content rules
						
Code generation and software development use Public AI tool usage
						
Escalation and exception handling
					

				

				

					The goal is not to slow AI adoption. The goal is to prevent avoidable mistakes that create legal,
					privacy, security, or reputational exposure.
				

			
		
	

	

		

			

				
AI Risk Assessment
				

					We help assess AI risks by use case, business impact, data sensitivity, system exposure, and control
					maturity.
				
			

			


				
Risk areas may include:

				


					

						
Sensitive data leakage
						
Unauthorized access or excessive permissions
						
Unreliable or misleading outputs
						
Customer-impacting decisions
						
Operational dependency on ai outputs
						
Vendor and third-party ai risk
					

					

						
Model misuse or abuse
						
Privacy and confidentiality exposure
						
Intellectual property risk
						
Compliance and audit risk
						
Lack of human oversight
						
Unsafe automation or agentic actions
					

				
				

					Each risk is documented in a way leadership, security, compliance, legal, and technical teams can
					act on.
				
			
		
	

	

		

			

				
AI Risk Register Development
				

					We help organizations create an AI risk register that tracks risk ownership, likelihood, impact,
					control status, remediation plans, and review cadence.
				
			

			


				
A practical AI risk register may include:

				


					

						
AI system or use case name 
						
Business owner
						
Technical owner
						
Data classification
						
Risk description
						
Affected stakeholders
						
Likelihood and impact
					

					

						
Existing controls
						
Control gaps
						
Remediation plan
						
Target date
						
Residual risk
						
Approval status
						
Review frequency
					

				

			
			

				This gives leadership a structured view of AI exposure instead of scattered concerns and informal
				decisions.
			
		
	

	

		

			

				
AI Policy and Procedure Development
				

					Digital Warfare can create or update AI governance documentation to fit your operating environment.
				
			

			


				
Common documents include:

				


					

						
AI Governance Policy
						
AI Acceptable Use Policy
						
Generative AI Usage Policy
						
AI Risk Management Procedure
						
AI Vendor Review Procedure
						
AI System Approval Procedure
					

					

						
AI Data Handling Standard
						
AI Incident Response Procedure
						
AI Human Oversight Procedure
						
AI Logging and Monitoring Standard
						
AI Model and Use Case Inventory Procedure
						
AI Exception Management Procedure
					

				
				

					Each document is written to be usable by real teams, not just filed away for compliance.
				

			
		
	

	

		

			

				
AI Vendor Risk Management
				

					Many organizations inherit AI risk through third-party vendors and SaaS platforms.
				
				

					We help review AI vendors and AI-enabled services for security, privacy, contractual, operational,
					and governance concerns.
				
			

			


				
Vendor review areas may include:

				


					

						
AI functionality and data usage
						
Customer data processing
						
Model training and retention practices
						
Data sharing and sub-processors
						
Privacy and confidentiality controls
						
Security documentation
					

					

						
Access control and logging
						
Breach notification expectations
						
AI output reliability and limitations
						
Contractual protections
						
Regulatory and customer assurance needs
						
Exit strategy and data deletion expectations
					

				
				

					This helps procurement, legal, security, and compliance teams make better decisions before AI
					vendors become embedded in business operations.
				

			
		
	

	

		

			

				
Shadow AI Discovery and Control
				

					Employees often adopt AI tools before the organization has approved them.
				
				

					We help organizations identify and manage shadow AI risk through policy, discovery, education,
					control design, and approval workflows.
				
			

			


				
Focus areas include:

				


					

						
Public AI tool usage
						
Browser-based AI tools
						
Employee productivity tools
						
Unsanctioned AI plugins
						
AI features inside SaaS platforms
					

					

						
Unapproved data uploads
						
Confidential information exposure
						
Business unit exceptions
						
Approved tool alternatives
						
Escalation and review process
					

				
				

					The goal is not to punish innovation. The goal is to give employees safe, approved paths for using
					AI.
				

			
		
	

	

		

			

				
AI Control Framework Mapping
				

					We help map AI governance controls to existing security, privacy, and compliance programs.
				
			

			


				
Depending on your environment, mapping may support::

				


					

						
NIST AI RMF
						
ISO/IEC 42001 readiness
						
NIST CSF
						
NIST SP 800-53r5
						
ISO 27001
						
SOC 2
					

					

						
Privacy programs
						
Vendor risk programs
						
Secure sdlc programs
						
Internal audit requirements
						
Board reporting expectations
					

				
				

					This helps your organization show that AI risk is being managed through recognizable, structured
					controls.
				

			
		
	

	

		

			

				
AI Incident Response Readiness
				

					AI-related incidents require clear escalation paths, technical review, legal input, communications
					planning, and business decision-making.
				
			

			


				
We help define how your organization should respond to AI-related events such
					as:

				


					

						
Sensitive data entered into unauthorized AI tools
						
AI system data exposure
						
Prompt injection or AI workflow abuse
						
Unauthorized AI tool actions
						
Incorrect AI outputs causing business impact
					

					

						
Vendor AI incidents
						
Customer-facing AI failures
						
Model or retrieval manipulation
						
Unexpected AI behavior
						
Policy violations involving AI systems
					

				
				

					Your incident response process should account for AI-specific evidence, ownership, containment,
					communications, and remediation.
				

			
		
	

	

		

			

				
Human Oversight and Accountability
				

					AI governance should make clear where human review is required, who is accountable, and when AI outputs cannot be used without validation.
				
			

			


				
We help define oversight expectations for:

				


					

						
Customer-impacting outputs
						
Regulated or sensitive decisions
						
Financial, legal, healthcare, security, or hr-related use cases
						
Ai-generated code
						
Ai-generated reports or analysis
					

					

						
Automated workflows
						
Agentic systems with tool access
						
High-risk ai use cases
						
Exceptions and escalations
					

				
				

					Human oversight reduces reliance on blind automation and helps preserve accountability.