AI Audit: Find and Govern the AI Already Inside Your Business
Audit shadow AI usage, evaluate model reliability, secure corporate data leaks, and establish compliant, risk-governed AI operations aligned with standard security and regulatory frameworks.
The Shadow AI Problem
As organizations eagerly integrate Generative AI (LLMs, automated agents) into their software workflows, they expose themselves to unprecedented vectors of risk. Employees routinely submit proprietary code, client profiles, and sensitive financial spreadsheets to public cloud endpoints without governance structures. This "Shadow AI" adoption bypasses standard corporate firewalls and creates significant data liability.
Security Exposure
An audit is the first line of defense. Without governance, proprietary intellectual property, employee credentials, or client records can be absorbed by third-party training cycles, leading to compliance breaches under national privacy laws.
What Our AI Audit Inventories
Greencon.ai conducts a multi-dimensional diagnostic audit across your enterprise workloads:
1. Shadow AI & Tools Inventory
We trace internal traffic patterns, browser activities, and API keys to inventory every AI utility, wrapper, and SaaS integration actively accessed by your staff. We identify exactly who is using what, and where data is flowing.
2. Data Leakage & Pipeline Analysis
We audit data ingestion pipelines connecting your internal databases to external models. We map data flows to ensure personal identifiable information (PII) is securely masked or tokenized, preventing exposure during third-party processing.
3. Model Security & Prompt Injection Audit
For custom in-house AI deployments, we evaluate vulnerability profiles against prompt injection, adversarial overrides, and unauthorized system access. We ensure system guardrails are robust enough to handle unexpected queries.
4. Bias, Hallucination, & Drift Diagnostics
We audit model accuracy outputs. We check predictive and generative models for bias deviations, hallucination thresholds, and evaluate MLOps performance drift over operational timelines to ensure decisions remain reliable and ROI-positive.
Security, Privacy & Compliance Exposure
Under the mandates of the National Privacy Commission (NPC) in the Philippines and the Data Privacy Act of 2012, organizations must verify that automated decision systems (ADS) do not violate citizen privacy rights or process personal data without valid consent structures. We provide complete privacy impact assessments tailored specifically for AI model pipelines.
Your AI Governance Framework
- Shadow AI Risk Map: Complete inventory of unsanctioned AI applications detected within your networks and associated risk grades.
- Data Ingestion Review: Analysis of data pipeline transit security, with actionable masking and encryption suggestions.
- Enterprise AI Policy Templates: Compliant governance templates establishing access control tiers, approved tools lists, and training protocols.
Frequently Asked Questions
What is Shadow AI?
Shadow AI refers to the unsanctioned use of artificial intelligence tools, platforms, or APIs by employees within an organization without the approval, oversight, or knowledge of the IT or security department.
How does an AI audit support compliance with the Data Privacy Act (DPA)?
An AI audit maps how personal data is ingested, processed, and stored by AI models, ensuring appropriate masking, anonymization, and security measures are in place to comply with NPC guidelines and DPA 2012.
Can an AI audit evaluate custom-built models?
Yes, the audit assesses custom model architectures for prompt injection vulnerabilities, output reliability, bias drift, and data pipeline integrity.