Compliance means handling data, systems, and workplace documents according to applicable laws, regulations, standards, policies, and governing requirements. In AI-assisted IT work, it helps prevent sensitive information from being exposed or mishandled. section 1.6.1 section 16.1.1
Use compliance as a practical checkpoint:
- Check company policies before sending confidential information to a cloud AI service. section 1.6.1
- Confirm whether submitted data may be retained, and use a private subscription or self-hosted instance for protected data. section 1.6.1
- Review AI-generated configuration checks, database decisions, and HR documents with qualified human judgment. section 10.6.4 section 16.1.1
A safer documentation workflow
A manager can ask an AI tool to review a warning document against company policies and legal requirements, identify missing details or bias, and suggest clearer wording. The compliance checker is an initial safeguard, not a replacement for legal counsel. section 16.1.1
Table 1.6 Fundamental considerations for AI data security
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Concern
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Effect
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|---|---|
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Data movement
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Information sent to AI services leaves your corporate network for cloud processing, potentially exposing sensitive data.
|
|
Data retention
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AI providers may store and retain submitted data for model training unless explicitly disabled.
|
|
Compliance
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Data handling must align with regulations like GDPR, the Health Insurance Portability and Accountability Act (HIPAA), Service Organization Control 2 (SOC 2), ISO 27001, and industry-specific standards.
|
|
Model security
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Unauthorized access or adversarial attacks can compromise AI models, leading to data leakage, biased outputs, or manipulation.
|
|
Access control
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Improper identity and access management (IAM) policies can result in unauthorized users gaining access to sensitive AI-generated insights.
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|
Encryption and data privacy
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Not all AI services offer end-to-end encryption or customer-managed encryption keys (CMEK), affecting data confidentiality.
|
|
Auditability and logging
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Lack of robust logging can make it difficult to track data usage, detect anomalies, or comply with security audits.
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Use the security table to compare data movement, retention, access control, encryption, model security, and compliance concerns, including GDPR, HIPAA, SOC 2, and ISO 27001. section 1.6.1
