AI DataFireWall™ FAQ: All You Need To Know

Contextul’s AI DataFirewall™ leverages advanced pattern-matching techniques originally developed for our PrivacyManager™ DSAR platform, adapting these capabilities to address modern AI-related data risks.

The PrivacyManager™ system uses sophisticated pattern recognition to identify personal information across documents during Data Subject Access Request (DSAR) processing, ensuring accurate redaction of third-party data while maintaining compliance.

This foundational technology enables automated detection of sensitive data types like names, IDs, and financial details within complex file structures.

AI DataFirewall™ applies these proven pattern-matching methods in real-time AI interactions, scanning both text prompts and file attachments (Word, Excel, PDFs, etc.) for 27+ categories of sensitive information.

By extending PrivacyManager™’s regulatory-compliant detection logic already refined for GDPR, CCPA, HIPAA, and other global privacy frameworks the system pseudonymises detected personal data before transmission to AI platforms like ChatGPT. This approach maintains

PrivacyManager™’s crucial balance between automation accuracy and data protection, now applied to a new attack surface where employees might inadvertently share sensitive information with external AI systems.

The shared pattern-matching core ensures consistent data handling across both compliance-driven DSAR responses and proactive AI data leakage prevention.

1. What are the advantages of using AI DataFireWall compared to an offline or private LLM?

AI DataFireWall™ offers a unique middle ground that combines the best of both worlds: the productivity of external AI and LLM platforms (like ChatGPT) with the security of an offline or private LLM. Unlike an offline LLM, which requires significant resources to set up, maintain, and train locally think hardware, IT staff, and constant updates AI DataFireWall™ lets you use powerful, cloud-based LLMs without sacrificing data security. It acts as a protective layer, scanning and pseudonymizing sensitive data in prompts and attachments before they leave your organisation, then reversing the process for safe delivery back to you. This means you get real-time access to cutting-edge AI tools without the risk of leaking personal or corporate data, all while avoiding the complexity and cost of running a private LLM. Plus, it’s built to integrate with multiple platforms, giving you flexibility that a single offline LLM might not offer.

 

2. Explain in easy steps how the product will be implemented into an organisation assuming you are using a Docker instance as the backbone.

Implementing AI DataFireWall™ with a Docker instance is straightforward and designed to fit smoothly into your existing setup. Here’s how it works in simple steps:

Step 1: Prepare Your Environment

Your IT team ensures you have Docker installed on a server or cloud instance (e.g., AWS, Azure). You’ll need basic resources like CPU, memory, and network access nothing too fancy!

Step 2: Pull the AI DataFireWall™ Image

We provide a Docker image (a pre-packaged version of the software). Your team runs a simple command like docker pull Contextul/AI DataFirewall™ to download it from our secure repository.

Step 3: Configure the Container

Launch the Docker container with a command like docker run -d -p 8080:8080 Contextul/AI DataFirewall™. You’ll tweak a few settings like pointing it to your AI platforms (e.g., ChatGPT) and defining what data to scan using a user-friendly config file we provide.

Step 4: Route Traffic Through AI DataFireWall™

Set up your network so user requests to AI platforms go through the Docker instance first. This could mean updating your proxy settings or firewall rules your IT crew will know the drill.

Step 5: Test and Go Live

Send a few test prompts and files through the system. You’ll see sensitive data (like names or financial info) get pseudonymised before it leaves, and the response comes back clean. Once you’re happy, roll it out to everyone!

It’s quick to deploy, and our support team (reach Darren at +447973 411504 or darren@contextul.io) is here to help every step of the way.

3. Our organisation has Co-Pilot enabled across the entire organisation and we are happy with the policies from Microsoft that that our data will remain safe, why do I need this tool?

We love that you’re enjoying Co-Pilot it’s a fantastic tool! Microsoft does a great job with its built-in policies to keep data safe within its ecosystem. However, AI DataFireWall™ adds an extra layer of protection that goes beyond what Co-Pilot alone can do. While Microsoft’s policies ensure data stays secure within their controlled environment, they don’t cover every scenario like when users copy sensitive data from Co-Pilot and paste it into external platforms (e.g., ChatGPT or Claude) that aren’t under Microsoft’s umbrella. AI DataFireWall™ steps in here, scanning prompts and attachments in real time to catch and pseudonymise sensitive info before it leaves your organisation, no matter where it’s headed. It’s like a safety net for those “oops” moments, ensuring compliance with strict regulations (GDPR, HIPAA, etc.) and keeping your data secure even outside Microsoft’s walls. Think of it as a teammate to Co-Pilot, not a replacement!

4. What about commercially sensitive information and legal hold material, or other sensitive information that doesn’t follow a pattern, how will AI DataFireWall deal with that?

AI DataFireWall™ is designed to handle all kinds of sensitive information, even the tricky stuff that doesn’t fit neat patterns like trade secrets, legal hold documents, or unique corporate data. It uses advanced scanning technology to search prompts and attachments (Word, Excel, PDFs, etc.) for personal and sensitive info based on 27+ legal jurisdictions and regulations (e.g., GDPR, HIPAA, CCPA). For data that doesn’t follow a standard format, you can create custom patterns tailored to your organisation’s needs think project codes, confidential memos, or legal terms. The system categorises and pseudonymises this data (e.g., replacing “Project X Blueprint” with a random code) before it goes to an AI platform, then swaps it back when the response comes to you. This keeps your commercially sensitive or legally protected info safe without disrupting your workflow. Plus, as we roll out own LLM and support for others like Claude, Llama, Grok we’re adding even smarter detection features stay tuned!

5. What does AI DataFireWall offer that I can’t do with my existing cloud security and DLP policies?

Your current cloud security and DLP policies are likely doing a solid job protecting data at rest or in transit within your network. But AI DataFireWall™ takes it up a notch by focusing specifically on the AI and LLM platforms your team uses every day. Unlike traditional DLP, which might block or flag data broadly, AI DataFireWall™ sits between your users and external AI tools, scanning prompts and attachments in real time to catch sensitive info before it leaks. It then pseudonymises that data something most DLP solutions don’t do letting you use powerful AI platforms safely without banning them outright. It’s also built to handle the unique risks of AI interactions (like accidental oversharing) and supports a growing list of platforms (ChatGPT now, Claude and Co-Pilot soon), giving you flexibility that generic cloud security might not offer. It’s a specialised boost to your existing setup!

6. Have you considered how you would put a service like this into our back end i.e., if a user goes to ChatGPT, or whatever, in our environment, then any data that people put in is automatically routed through a filter like this (rather than the user having to go to a different URL)?

Absolutely, we’ve got you covered! AI DataFireWall™ is designed to integrate seamlessly into your back end, so users don’t need to change their habits or visit a special URL. Here’s how it works: we deploy it as a proxy or gateway in your network (like with that Docker instance we mentioned). Your IT team sets up routing rules say, in your firewall or proxy server so that any traffic heading to AI platforms like ChatGPT automatically passes through AI DataFireWall™ first. It scans and pseudonymises sensitive data on the fly, then forwards it to the platform, all without users noticing a thing. Responses come back through the same path, getting de-pseudonymised for a smooth experience. It’s invisible to your team, keeps them productive, and ensures every interaction is secure no extra steps required!

7. We are considering using Microsoft Purview to help with DLP with AI tools how would this product be positioned against that? What are the complementary benefits, or not, of using AI DataFireWall in conjunction with Purview?

Microsoft Purview is a fantastic choice for broad data loss prevention (DLP) across your Microsoft 365 ecosystem, and AI DataFireWall™ complements it beautifully rather than competing. Purview excels at classifying, labelling, and protecting data within Microsoft tools like Co-Pilot, Teams, and SharePoint, using policies to block or alert on sensitive data movement. AI DataFireWall™, on the other hand, specialises in securing interactions with external AI and LLM platforms (e.g., ChatGPT, soon Claude and others) that Purview doesn’t fully cover. It scans prompts and attachments in real time, pseudonymising sensitive data before it leaves your network something Purview doesn’t do natively for non-Microsoft platforms. Together, they’re a powerhouse: Purview locks down your internal data, while AI DataFireWall™ safeguards external AI use. You get Purview’s compliance muscle (e.g., GDPR, HIPAA) plus our flexibility for multi-platform AI safety. It’s a perfect tag team!

8. How does AI DataFireWall ensure compliance with specific regulations like GDPR or HIPAA?

AI DataFireWall™ is built with compliance in mind, covering 27+ legal jurisdictions including GDPR, HIPAA, CCPA, and more. It scans prompts and attachments for personal and sensitive data like names, health records, or financial details that these regulations protect. Once detected, it does a look up against our 32 billion+ names database and pseudonymises that data (e.g., turning “John Doe” into “Gary Webb”) before it reaches an AI platform, ensuring it never leaves your organisation in a recognisable form. This aligns with GDPR’s data minimisation rules and HIPAA’s patient privacy requirements. Plus, you can customise detection patterns to match your specific compliance needs, and we’re always updating to stay ahead of new regulations. It’s your compliance safety net!

9. Can AI DataFireWall handle large volumes of data or users without slowing things down?

Yes, it’s designed to scale! Running on a Docker instance, AI DataFireWall™ can handle high traffic and large datasets efficiently. The scanning and pseudonymisation happen in real time, optimized to keep latency low think milliseconds, not seconds. Whether you’ve got dozens or thousands of users sending prompts and files, it adjusts to your load. If you’re expecting a big spike, just beef up your Docker resources (more CPU or instances), and it’ll keep humming along. We’ve tested it with heavy workflows, and it’s built to keep your team moving fast and secure.

10. What kind of support do you offer if we run into issues during setup or use?

We’ve got your back! Our founder, Darren Wray, is just an email (darren@contextul.io) away, and our support team is ready to help 24/7. You’ll get detailed setup guides, live chat for quick fixes, and personalized assistance for trickier issues. Whether it’s tweaking your Docker setup or fine-tuning data patterns, we’re here to make it smooth and stress-free. Plus, we’re always improving based on your feedback Contextul is all about partnership!

11. How does AI DataFireWall detect sensitive data that’s not in a standard format, like internal project names?

AI DataFireWall™ goes beyond standard patterns (like credit card numbers) with customizable detection. You can define your own patterns like “Project Alpha” or “Confidential Q1 Plan”in the system. It’ll scan prompts and files for these unique terms, categorize them as sensitive, and pseudonymize them before they hit the AI platform. It’s flexible enough to learn your organisation’s lingo, so your proprietary data stays just as safe as regulated info. We’re also working on smarter AI-driven detection for even better coverage watch this space!

12. Is AI DataFireWall compatible with cloud platforms like AWS or Azure?

You bet! Since it runs in a Docker container, AI DataFireWall™ is cloud-agnostic it works great on AWS, Azure, Google Cloud, or even your own servers. You can spin it up in an EC2 instance, Azure Container Instances, or wherever you’re comfy. It integrates with your network setup, so whether your users are on-prem or in the cloud, it’ll protect their AI interactions. Deployment’s a breeze, and it plays nice with your existing cloud security tools.

13. What happens if an AI platform rejects pseudonymised data?

Good question! Most AI platforms (like ChatGPT) handle pseudonymised data just fine since it’s still coherent text just with sensitive bits swapped out (e.g., “Steve Watson” instead of “John Doe”). AI DataFireWall™ ensures the context stays intact, so the platform can still process it. If a platform ever balks, we’ll work with you to adjust the pseudonymisation rules or tweak the integration. As we add support for more LLMs (Claude, Co-Pilot, etc.), we’re testing to keep compatibility rock-solid. You won’t be left hanging!

14. Can we monitor what data AI DataFireWall is catching and pseudonymizing?

Absolutely! AI DataFireWall™ comes with a dashboard where you can see what’s being scanned, flagged, and pseudonymised in real time. You’ll get logs showing which prompts or files had sensitive data, what was changed, and where it was headed. It’s all transparent, so you can audit compliance, spot trends, or tweak policies. Want more detail? We can customise reports to fit your needs just let us know!

15. How often do you update AI DataFireWall to keep up with new AI platforms or threats?

We’re on it constantly! Our team at Contextul rolls out updates regularly think quarterly for big features (like new LLM integrations) and as-needed for security patches or compliance shifts. Since it’s Docker-based, updates are a snap: pull the latest image, redeploy, and you’re current. We’re already planning support for Claude, Co-Pilot, and Gemini, plus smarter detection for emerging threats. You’ll always be ahead of the curve with us!

16. Does AI DataFireWall work with non-English languages or international data?

Yes, it’s global-ready! AI DataFireWall™ supports data in multiple languages and aligns with 27+ legal jurisdictions (e.g., EU GDPR, Japan’s APPI). It can scan and pseudonymise sensitive info like names or IDs in various scripts and formats. If your team’s multilingual, it’ll keep up, and you can add custom patterns for region-specific terms. It’s built to protect your data, no matter where your users are or what language they’re using.

17. What’s the cost model for AI DataFireWall—subscription, per-user, or something else?

We keep it flexible to fit your budget! AI DataFireWall™ is subscription-based, with pricing tiers based on the number of users and data volume you need to protect. Think of it like a “pay for what you use” model small teams get a lightweight plan, while big orgs can scale up. No hidden fees, and you get all features (scanning, pseudonymisation, integrations) included. Chat with Rob (robert@contextul.io) for a quote tailored to your setup it’s all about value, not surprises!

 

18. Can AI DataFireWall integrate with our existing identity management system?

Definitely! AI DataFireWall™ can hook into your identity management setup (like Active Directory or Okta) to ensure only authorised users access AI platforms through it. You’ll set up authentication in the Docker config, linking it to your SSO or user database. This keeps security tight and lets you control who gets to use it, all while keeping the user experience seamless. It’s a natural fit with your current controls!

19. How does AI DataFireWall handle encrypted files or data?

AI DataFireWall™ is smart about encryption. If a file’s encrypted (e.g., a password-protected PDF), it won’t try to crack it that’s not its job! Instead, it’ll flag it as “unscannable” and either block it or let it pass based on your policy (you decide). For unencrypted files, it scans and pseudonymises as usual. If you’re sending encrypted data to AI platforms often, we can work with you to preprocess it safely or adjust rules. It’s all about keeping your workflow secure and smooth!

20. What’s the setup time for AI DataFireWall in a mid-sized organisation?

For a mid-sized org (say, 100-500 users), setup takes just a few hours to a day, depending on your IT team’s speed. With Docker, it’s mostly: 1) deploy the container (30 mins), 2) configure network routing (1-2 hours), and 3) test it out (1 hour). Add a bit more time if you’re customising patterns or integrating with identity systems, but you’re looking at a morning or afternoon total. We’ll guide you through it, so you’re up and running fast!

21. Can we use AI DataFireWall for on-premises AI tools, not just cloud-based ones?

Yes, it’s versatile! While it’s optimized for cloud AI platforms, you can point AI DataFireWall™ at on-premises AI tools too. Just route traffic from your users to the Docker instance, then to your local AI system. It’ll scan and pseudonymize data the same way, keeping sensitive info safe even within your walls. It’s a great way to unify security across all your AI use, cloud or not!

22. How does AI DataFireWall protect against insider threats or intentional leaks?

AI DataFireWall™ is your insider threat shield! It doesn’t care if a leak’s accidental or on purpose it scans every prompt and attachment leaving your network for sensitive data. If someone tries to send proprietary info to an AI platform, it gets pseudonymised (e.g., “Secret Formula” becomes “CodeXYZ”) before it goes out, rendering it useless to outsiders. You’ll also see it in the logs, so you can follow up. It’s proactive protection that stops leaks cold, no matter the intent!

23. What kind of training do our IT staff need to manage AI DataFireWall?

No PhD required! If your IT team knows Docker basics (like running containers and editing config files), they’re 90% there. We provide a quick-start guide and a 1-hour training session (live or recorded) covering setup, pattern customization, and monitoring. It’s intuitive think “set it and forget it” with occasional tweaks. For deeper dives, Rob and the team are a call away. Your staff will be pros in no time!

 

24. Can AI DataFireWall scale with our organisation as we grow?

Totally! AI DataFireWall™ grows with you. Since it’s Docker-based, scaling is as easy as adding more containers or boosting server resources (CPU, memory). Whether you jump from 50 to 5,000 users, it handles the load without breaking a sweat. Pricing scales too, so you only pay for what you need. It’s built to keep pace with your success, securely and smoothly!

25. What’s the roadmap for AI DataFireWall—what new features are coming?

We’re excited about what’s next! Beyond current ChatGPT integration, we’re adding support for Claude, Co-Pilot, Gemini, and more LLMs soon. You’ll see smarter AI-driven detection for tricky data via our own proprietary LLM, deeper analytics in the dashboard, and tighter integrations with tools like Purview. We’re also exploring automated policy suggestions based on your usage. Updates roll out regularly, and we’d love your input—Contextul’s all about building what you need next!