AI Security Assessment Services
Assess AI security risk across AI inventory, model governance, data exposure, vendor usage and regulatory readiness. Artificial Intelligence is now embedded into business workflows, SaaS platforms, cloud services, security operations, document processing, customer communication and internal automation. The risk is no longer limited to whether AI works. Organizations must understand what data AI systems process, who owns the system, which vendors are involved, what controls exist, and how AI outputs are reviewed, monitored and governed.
ServQual helps organizations assess AI security, privacy and compliance risks using a structured approach across governance, security controls, data protection, vendor risk and operational assurance. With SUSAN, teams can connect AI risk findings to control mapping, AI Risk Scoring, remediation tracking and continuous compliance visibility.
Why AI Security Assessment Matters
AI adoption often grows faster than governance. Business teams may use AI tools for email drafting, customer support, vendor onboarding, document review, coding assistance, analytics or workflow automation before security and compliance teams have visibility.
This creates risks such as:
Sensitive data leakage into AI tools
Poor control over AI-generated outputs
Unapproved AI usage across teams
Prompt injection exposure
Weak AI vendor governance
Model governance gaps
Incomplete evidence for AI risk decisions
Limited human review of AI-assisted workflows
Privacy and data protection concerns
Regulatory readiness gaps for frameworks such as the EU AI Act
An AI Security Assessment helps organizations identify these risks before they become audit findings, privacy incidents, customer trust issues or security events.
What ServQual Helps With
Identify AI systems, AI-enabled SaaS tools, internal AI workflows, third-party AI services and business processes where AI is used.
Review AI ownership, approval processes, acceptable use rules, human review, escalation paths, evidence requirements and leadership reporting.
Review third-party AI vendors, AI-enabled SaaS platforms, data processing terms, access permissions, logging, retention, regional processing and assurance evidence.
Review whether AI systems have documented purpose, scope, owners, risk classification, monitoring approach, human review requirements and evidence of control operation.
Support AI system inventory, classification, governance, transparency, monitoring and evidence readiness for organizations preparing for EU AI Act obligations.
Assess AI risks across data exposure, access control, output reliability, vendor dependency, model usage, monitoring, compliance impact and operational accountability.
Assess whether sensitive data, personal data, customer records, credentials, financial information or business-confidential content could be exposed through AI prompts, integrations or automated workflows.
Assess whether AI systems, copilots, chatbots or workflow assistants are exposed to prompt injection, unsafe instructions, unsupported claims, data leakage or unreliable outputs.
Assess AI processing against privacy, security and governance expectations, including data minimization, lawful processing, retention, access control, transparency and audit evidence.
Create a prioritized action plan covering AI governance gaps, control weaknesses, data protection risks, vendor risk, monitoring gaps and policy improvements.
Key AI Security Risks We Assess
AI tools may process personal data, customer data, internal documents, credentials, regulated information or confidential business content without appropriate controls.
AI-generated answers may include unsupported claims, incorrect compliance statements or misleading recommendations if not reviewed and mapped to trusted sources.
AI systems may lack clear ownership, approval, usage boundaries, monitoring, review workflows and evidence of control effectiveness.
AI usage may not be visible to SOC, GRC, cloud security or privacy teams, making incidents difficult to detect and investigate.
Attackers or untrusted content may manipulate AI systems into ignoring instructions, revealing sensitive information or producing unsafe outputs.
Third-party AI services may introduce data residency, retention, access control, model training, subprocessors or contractual assurance risks.
AI processing may affect privacy obligations where personal data is collected, analyzed, summarized, transferred, retained or disclosed.
How SUSAN Supports AI Security Assessment
SUSAN helps organizations turn AI risk assessment into a structured governance and compliance workflow.
With SUSAN, teams can:
Use a Unified GRC Dashboard for leadership visibility
Maintain AI risk visibility through AI Risk Scoring
Support continuous monitoring and evidence for audit readiness
Map AI risks to controls, remediation and compliance requirements
Connect AI risk with broader cybersecurity and compliance posture
Track governance gaps across AI usage, security, privacy and third-party risk
Support EU AI Act readiness through governance, classification, transparency and monitoring workflows
AI Security Assessment Approach
Identify AI systems, AI-enabled applications, business workflows, vendors, data sources, owners and current usage patterns.
Classify AI systems by business purpose, data sensitivity, regulatory impact, vendor dependency, user group and operational risk.
Evaluate AI risks across data leakage, prompt injection, access control, vendor assurance, model governance, privacy impact, monitoring and evidence readiness.
Map findings to governance, security, privacy, vendor risk, cloud security and compliance control areas.
Prioritize remediation based on risk severity, business impact, regulatory exposure, data sensitivity and control maturity.
Provide a clear AI security assessment report with findings, risk ratings, recommended controls, ownership and remediation roadmap.
Example Assessment Areas
AI inventory and ownership
Prompt injection exposure
AI vendor and third-party risk
Data leakage and sensitive data exposure
AI acceptable use and governance
Model governance and control ownership
Human review and approval workflow
Logging, monitoring and audit evidence
Privacy and data protection impact
EU AI Act readiness
Cloud and SaaS AI usage
AI output review and source verification
Incident response for AI-related events
Business Outcomes
Clear visibility of AI usage across teams and systems
Reduced risk of sensitive data exposure
Stronger AI governance and accountability
Better AI vendor risk management
Practical remediation roadmap for AI security gaps
Improved readiness for EU AI Act obligations
Better evidence for audit, customer assurance and leadership reporting
Continuous AI risk visibility through SUSAN
Who Needs This Service?
This service is suitable for:
Organizations using Generative AI tools
SaaS and technology companies embedding AI features
Security teams assessing AI risk exposure
Privacy teams reviewing AI processing
GRC teams preparing AI governance controls
Regulated enterprises using AI in business workflows
Cloud teams managing AI workloads and AI-enabled SaaS tools
Organizations preparing for EU AI Act readiness
Businesses concerned about AI data leakage or prompt injection
FAQ
Most frequent questions and answers
An AI Security Assessment reviews how an organization uses AI systems, AI-enabled SaaS tools and AI workflows. It identifies risks related to data leakage, model governance, prompt injection, vendor risk, access control, privacy and compliance readiness.
AI security matters because AI tools may process sensitive data, generate unsupported outputs, connect to business workflows, interact with third-party systems and create privacy, security and governance risks if not controlled.
Yes. ServQual helps assess AI ownership, approval workflows, acceptable use rules, human review, evidence requirements, monitoring and governance accountability.
SUSAN supports AI risk management through AI Risk Scoring, control mapping, remediation tracking, Unified GRC Dashboard, Continuous Monitoring & Evidence and compliance visibility.
Typical review areas include AI inventory, data leakage, prompt injection, model governance, AI vendor risk, privacy impact, access control, logging, monitoring, human review and EU AI Act readiness.
Yes. The assessment can support AI inventory, classification, governance, transparency, monitoring and evidence readiness for organizations preparing for EU AI Act obligations.
Yes. The assessment reviews whether AI systems, copilots, chatbots or workflow assistants may be exposed to prompt injection, unsafe instructions or sensitive data disclosure.
No. AI Security Assessment is broader and covers AI governance, inventory, data leakage, vendor risk and compliance readiness. LLM Security Testing is more technical and focuses on prompt injection, jailbreak testing, guardrails and sensitive data leakage testing.
No. RAG Security Assessment focuses specifically on retrieval pipelines, vector databases, source governance, chunk poisoning, secure document ingestion and retrieval audit logs.
This service is useful for security teams, privacy teams, GRC teams, cloud teams, AI product teams, SaaS providers and regulated enterprises using or planning to use AI.
Start Your AI Security Assessment
Identify AI risk before it becomes a security, privacy or compliance incident.
ServQual and SUSAN help organizations assess AI usage, strengthen governance, reduce data exposure and build audit-ready AI security controls.
