AI Content Filtering to Protect Sensitive Data
AI systems filter data to prevent leaks or improper sharing of sensitive information.
Plain language
AI content filtering involves using smart technology to identify and prevent the sharing of sensitive information, like personal details or confidential business data. This is important because if sensitive data is leaked, it could lead to identity theft, financial loss, or harm to your organisation's reputation.
Framework
ASD Information Security Manual (ISM)
Control effect
Preventative
Classifications
NC, OS, P, S, TS
ISM last updated
Nov 2025
Control Stack last updated
19 Mar 2026
E8 maturity levels
N/A
Guideline
Guidelines for software developmentOfficial control statement
Content filtering is implemented by artificial intelligence applications to detect and block sensitive data exposure and improper output.
Why it matters
Without AI content filtering, sensitive data can be leaked via AI prompts or outputs, causing legal exposure, financial loss and reputational harm.
Operational notes
Tune and update AI content filters, test with red-team prompts, and review filter and audit logs to verify sensitive data is detected and blocked.
Implementation tips
- Business owners should partner with their IT team to identify what constitutes sensitive data for their organisation. This involves listing types of information, such as customer contact details or financial records, that need protection.
- IT teams should install AI content filtering tools to monitor and control data flows. This can be done by researching reliable software providers and setting up a trial period to test the tool's effectiveness.
- Managers should train their staff to recognise and handle sensitive information properly. Arrange regular workshops or online training sessions that highlight what constitutes sensitive data and how to avoid accidental sharing.
- Procurement teams should ensure that any AI filtering technology complies with current Australian privacy laws. This can be done by checking vendor guarantees and consulting legal advisors during the purchasing process.
- Security officers should routinely review and update AI data filtering settings to adapt to new threats. This requires setting up a monthly review schedule and adjusting filters based on the latest security insights.
Audit / evidence tips
- AskThe data inventory list: Request a document detailing what data is considered sensitive within the organisation GoodA detailed list updated within the past year, showing various protected data categories
- GoodA report noting the software name, installation date, configured settings, and any updates applied
- AskThe staff training records: Request records of training sessions held on data sensitivity GoodDocumented evidence of recent training with high staff attendance and positive feedback on understanding
- GoodCertificates confirming compliance with relevant Australian privacy laws, dated within the last two years
- AskThe filter review schedule: Request a schedule or records of regular AI filter reviews GoodA clear review schedule documenting monthly checks and adjustments to the filtering settings based on new risks
Cross-framework mappings
How ISM-2094 relates to controls across ISO/IEC 27001, ISO/IEC 42001, Essential Eight, and ASD ISM.
ISO 27001
| Control | Notes | Details |
|---|---|---|
| layers Partially meets (1) expand_less | ||
| Annex A 8.12 | ISM-2094 requires AI applications to implement content filtering to detect and block sensitive data exposure and improper output | |
| handshake Supports (2) expand_less | ||
| Annex A 8.15 | ISM-2094 requires AI applications to filter content to detect and block sensitive data exposure and improper output | |
| Annex A 8.33 | ISM-2094 requires AI applications to filter content to prevent sensitive data leakage and improper disclosure in outputs | |
| extension Depends on (1) expand_less | ||
| Annex A 5.13 | ISM-2094 requires AI applications to detect and block sensitive data exposure and improper output via content filtering | |
These mappings show relationships between controls across frameworks. They do not imply full equivalence or certification.