ISM-2090 ASD Information Security Manual (ISM)

Rate Limiting for AI Model Inference Queries

Limit how often AI queries are run to prevent system overuse and improve efficiency.

Preventative Non-classified OFFICIAL: Sensitive PROTECTED SECRET TOP SECRET Contracts ASD Information Security Manual Governance Performance Secure coding

🏛️ Framework

ASD Information Security Manual (ISM)

🧭 Control effect

Preventative

🔐 Classifications

NC, OS, P, S, TS

🗓️ ISM last updated

Nov 2025

✏️ Control Stack last updated

22 Feb 2026

🎯 E8 maturity levels

N/A

Guideline

Guidelines for software development

Section

Artificial intelligence application development

Topic

Unbounded Consumption

Official control statement

Rate limiting is applied to inference queries for artificial intelligence models.

Source: ASD Information Security Manual (ISM)

Plain language

Rate limiting means setting limits on how often AI systems are allowed to process requests. This is important because without limits, the system could become overloaded, slow down, or even crash, causing disruptions and potentially leading to mistakes in important tasks.

Why it matters

Without rate limiting, AI inference APIs can be abused, driving up compute costs and causing service degradation or denial of service for legitimate users.

Operational notes

Monitor inference request rates and tune limits per client/model; log and review HTTP 429 events to detect abuse and adjust thresholds without blocking legitimate use.

Implementation tips

IT team should establish query limits: Determine the maximum number of requests that the AI system can handle without slowdowns. Analyse system performance data to set these limits effectively.
Procurement should coordinate with service providers: Engage with AI service suppliers to ensure they support rate limiting features. Confirm these features are included in contracts and service agreements.
Developers should integrate rate limiting in application code: Include features that monitor and cap requests. Use simple coding practices to prevent the system from processing too many requests at once, like setting time intervals or request counts.
System owners should monitor usage patterns: Continuously observe how often and for what purposes the AI is accessed. Adjust limits based on these insights to balance efficiency and accessibility.
Managers should educate staff: Conduct training sessions on why rate limits are in place. Use relatable examples to explain how these limits maintain system reliability and ensure fairness in access.

Audit / evidence tips

Ask: system logs showing query volume: Request records that display the number of AI queries over a specific period

Good: would show consistent adherence to set limits
Ask: documentation on rate limit settings: Request the file or record detailing the current rate limits in use

Good: includes both the numbers themselves and the rationale for their setting
Ask: contracts with service providers: Request copies of agreements with AI service suppliers

Good: is a legally binding document with relevant terms clearly outlined
Ask: incident reports related to overload: Request any logs detailing system overload or failures due to excessive queries

Good: would show prompt response and adjustments where necessary
Ask: training materials given to staff: Request copies of any presentations or guides on rate limiting

Good: includes materials with clear explanations and examples that resonate with everyday tasks

Cross-framework mappings

How ISM-2090 relates to controls across ISO/IEC 27001, Essential Eight, and ASD ISM.

These mappings show relationships between controls across frameworks. They do not imply full equivalence or certification.

ISO 27001

Control	Notes	Details
Partially meets (1)
Annex A 8.6	ISM-2090 requires rate limiting to be applied to AI model inference queries to prevent overuse and manage service availability