Gio The Dev .NET

Introduction 

Artificial Intelligence (AI) has rapidly shifted from an emerging technology to a mainstream force in industry. With more companies adopting AI, its presence across sectors has become nearly universal.  

In recent years, adoption has accelerated, with surveys showing that almost all organizations now use AI in some form, though many remain in early pilot stages.  

Yet, despite this remarkable growth, AI remains a relatively new technology, and its deployment raises challenges.  

Accountability for AI‑Generated Content 

Beyond technical hurdles such as data readiness, models and ROI, there is also a notable lack of comprehensive regulation. As industries race ahead with adoption, questions of governance, ethics, and oversight continue to lag, creating both opportunities and risks. 

Despite of lack of regulation, companies are legally accountable for any content produced and delivered by AI and have been advised by the federal government that AI-generated content should be identifiable.  

Although AI content seems to be unpredictable in some extent, developers are surrounded by tools that can minimize and prevent AI-content derail from what it is trained to do.  

In this article, we are going to explore tools and patterns that mitigate the risks of delivering AI-generated content.  

Overview of Azure AI Content Safety 

Microsoft has released several AI services within Azure AI Foundry including Azure AI Content Safety and Content Safety Studio.  

Instead of building their own AI tools, companies can leverage ready-to-go services that enable developers to rapidly experiment AI capabilities to identify harmful content and implement it through API integration. 

The following features are available within Azure AI Content Safety. 

Prompt Shields: Defending AI Systems Against Prompt Injection and Jailbreak Attacks  

Prompt Shields (or Jailbreak) help prevent harmful or adversarial content from reaching or influencing a model. They detect and block unsafe user prompts, images, or documents before the model processes them, and they also stop content that contains embedded harmful instructions from being analysed.  

When the shield identifies such content, it can block the model from generating a response. 

Blocklists: Enforcing Custom Safety Policies Through Term and Pattern Filtering 

Blocklist allows organisations to define and manage a list of terms (words, phrased or regex patterns) that should be flagged or filtered out when generating information or validating a user’s input.  

You can also upload your own blocklists to enhance the coverage of harmful content that’s specific to your use case. 

Groundedness Detection: Identifying Hallucinations and Unsupported Model Output 

Large-Language models (LLMs) are sophisticated algorithms to generate content based on a given context.   

They are getting increasingly accurate over the years, but still lack rails to prevent them from generating inaccurate information.  

In fact, model, prompt, context and data must be aligned to reduce the chances of hallucinations and even then, you need mechanisms to detect when the model’s output isn’t grounded in the provided information. 

Protected Material Detection: Safeguarding Against Copyrighted Text and Code Reuse 

Content generated by generative AI services can contain traces of authoritative source. 

Protected material detection for text detects protected material such as song lyrics, articles, recipes, or other selected web content, adding an additional layer to help organisations comply with copyright requirements.  

Similarly, Protected material detection for code protects organisation from generating protected code from existing GitHub repositories.  

Task Adherence: Ensuring Agent Actions Stay Aligned with User Intent 

Agentic systems have the ability to interact with tools based upon the user’s instructions and intent.  

However, there are situations where the agent misunderstands these instructions and takes inappropriate actions.  

Task Adherence helps prevent this by ensuring that an agent’s actions are invoked in accordance with the given context, turning behavioural inconsistencies and misalignments a factor of scalation with human-in-the-loop intervention. 

Custom Categories: Training Domain‑Specific Classifiers for Text and Image Safety 

This feature allows organisations to train their models to classify text and image content in custom categories.  

It provides a broader range of possibilities by using input data to train classifiers that can categorise documents using more advanced techniques, such as semantic text matching and image matching. 

Text Moderation: Detecting Harmful Content Across Four Risk Categories  

Azure AI Content Safety provides text moderation APIs that analyse content and classify harmful language across four categories: Hate, Sexual, Violence, and Self-Harm, assigning a severity rating on a 0-7 scale to indicate the level of risk. 

Image Moderation: Classifying Visual Harm and Handling Embedded Text via OCR 

• Azure AI Vision (Image Analysis OCR) 
• Azure AI Document Intelligence 
• Multimodal moderation 

Multimodal Moderation: Combining Image and Text Analysis for Context‑Aware Safety 

This service combines text and image moderation by analysing visual content and associated text together while preserving contextual meaning.  It effectively bridges the gap between standalone image and text moderation. 

Feature Summary: What an Azure AI Content Safety Fits into Your Pipeline

The table below lists Azure AI Content Search features and maps them to a primary use case and the traffic direction where they apply. 

• Direction: indicates where the control acts in your pipeline.  
  • Input: before the model (screening user prompts or uploaded content). 
  • Output: after generation (validating model responses).
• Agent Planning: when an agent takes an action (calling a tool).
• Use Case: what risk the feature mitigates. 

Solution	Use case	Direction
Prompt Shields	Detect and prevent a malicious user from exploiting a system vulnerability to elicit inappropriate behaviour. It could affect prompts or content in documents that are analysed by a model. It prevents user prompt injection attacks or jailbreaks:  – Attempt to change system rules – Embedding a conversation mock-up to confuse the mode –  Role-Play  – Encoding Attacks	Input / Output
Blocklist	Detect and prevent terms from being used, generate inappropriate information about competitors, block commands, URLs or pattern expressions.	Input / Output
Groundedness	Detect responses that aren’t based on the source materials provided.	Output
Protected material detection	Detect and prevent organisations from generating content protected by copyright.	Output
Task Adherence	Detect and prevent tool invocation misalignment with user intent, context and tool input/output.	Agent Planning
Custom Categories	Categorise text and image by using a custom dataset	Input
Text Moderation	Detect harmful content in chat or data.	Input / Output
Image Moderation	Detect harmful content in images, except embedded text.	Input / Output
Multimodal Moderation	Detect harmful content in images including embedded text.	Input / Output

Operationalisation: Putting Safety into Practice 

To operationalise your safe posture, introduce Azure AI Content Safety around your app lifecycle. At a minimum:  

• Input gates: Prompt Shields and Blocklists to reduce jailbreaks, indirect prompt injection, and policy‑specific terms before they ever reach your model. 
• Supervise agent actions: Task Adherence when building agentic systems. Use its signal to block or escalate tool calls that don’t align with user intent or context, keeping humans in the loop where necessary. 
• Moderate inputs and outputs: Text, Image, and Multimodal moderation with a threshold per harmful category according to your risk tolerance.  
• Reduce hallucination risk: Groundedness detection to flag inconsistent content when using retrieval‑augmented generation (RAG) or enterprise knowledge bases. 
• IP compliance: Protect Material Detection (text and code) to detect third-party text or GitHub code in generated outputs. 
• Edge cases: when built-in categories can’t support your policy, you can train your model using Custom categories. 

By applying these controls at the right points in your lifecycle, you strengthen the safety, reliability, and compliance of your AI applications – protecting users, systems, and your organisation from misuse or unintended behaviour. 

Conclusion

I have seen organisations developing their own guardrails and validators. This level of customisation often delays the adoption and delivery of AI solutions. 

With Azure AI Content Safety, organisations are equipped with powerful tools that contribute to safe and responsible AI use. From a single integration to agentic workflows, these tools can be easily incorporated into AI applications to ship fast while complying with policy and regulations.  

There is no one-size-fits-all solution. You must evaluate your current application state to identify process gaps. It is not just a matter of turning features on but integrating them into your workflows transparently without impacting performance or latency.  

In some cases, you may trade off streaming capabilities in exchange for safety.  

Some services and models are not Global Available (GA) yet, so confirm regional availability before use, as some of these services are in preview. The language support is primarily English, although other languages are supported. 

References

• Tech companies advised to label and ‘watermark’ AI-generated content – ABC News 
• What is Azure AI Content Safety? – Azure AI services | Microsoft Learn 
• Azure AI Content Safety documentation – Quickstarts, Tutorials, API Reference – Foundry Tools | Microsoft Learn