AI is starting to find its way into many enterprise workflows. Whether it’s to help drive analytics on business data, increase productivity with automation, provide a tool that can be leveraged by workforce members, personalize a customer’s experience or to provide decision making. By utilizing generative AI and AI agents you can start tying these capabilities into your enterprise.
When tying these capabilities into your enterprise you’re introducing data. There will be many ways data will enter an AI platform. Through a user’s prompts, training data, search queries, connecting applications or any other method that ties to your environment. Ensuring you have controls in place will help limit risk of exposing or misusing this data.
As always it’s best practice to leverage your standard security measures when applying AI to your business. Make sure you understand what kind of data a model is pulling from, who is able to leverage this technology and what kind of actions are being performed. A user could enter prompts into an AI model and pull information from sources they shouldn’t have access to. Attackers could utilize prompt injection techniques to take advantage of your AI application. Or you may have an agent leveraging permissions it doesn’t need to.
AI data security allows your business to secure data when training, prompting, generating and utilizing AI models and agents.
In this blog we’ll cover some of the basic concepts around AI data security.
- How do I know what risk I’m exposed to?
- What can I do to mitigate those risks?
- We’ll look at these questions and provide guidance on securing your data from start to finish.
What Is AI Data Security?
There are many types of data you will encounter when utilizing AI technologies. AI data security helps ensure that data that’s going into or is generated from your AI model is kept safe. From training your model to fine tuning it there will be data that needs to be classified and ensured safe. Users will begin to enter prompt data into your model as well. There can be times where this information needs to be protected from leaking out. Or maybe an AI agent is pulling from your database and ensuring that only certain information is made available to those who have proper permissions.
Not only do we need to think about the model but all of the pieces that make up an AI application. Your data source, ETL pipeline, vector database, Model, retrieval service, Application, Tools and Observability. Any of these points could be susceptible to leaking information if you aren’t taking the proper measures.
Let’s say you’ve created an AI application that searches over your company policies and customer support interactions. While your model is being utilized correctly you could be allowing customer sensitive information to be displayed to users who shouldn’t have access to those documents. Maybe your retrieval service isn’t checking user permissions before searching for information or you have too many documents within your vector database that a model can access.
Some Questions you can ask yourself when applying AI data security are:
- What data am I feeding into my AI platform? Is it ok for me to be doing this?
- Where is sensitive data being accessed from and processed?
- Who/What has access to this information? Users, models, applications, agents etc.
- Could there be data leakage through users prompting, search queries or responses?
- Am I monitoring the data flows and actions my model/agent is performing?
- What will happen if my data is breached, tampered with or misused?
There are many ways you can ensure AI data security for your business but making sure you ask yourself these questions and have a plan in place is critical.
Why AI Data Security Matters for Enterprises
Securing Confidential Data
You have a variety of confidential information within your business. Customers’ PII, Employee information, IP, Source Code just to name a few. You need to know where this data is being fed into your AI models and what kind of controls you can put in place to ensure it stays secure. You may need to implement controls such as data classification, Allowlist AI models and data loss prevention tools. Make sure you’re also aware of vendors and what they do with data you send to them.
Limiting Unauthorized Access to Data
If you’re leveraging an AI assistant you may find yourself being able to access information quicker than if you had to look it up manually. This can become a security risk if that AI application isn’t applying data permissions that a user already has. If a user cannot access a certain document they shouldn’t be able to retrieve that information from your AI assistant.
Reducing the Risk of AI Threats
Similar to any other Application Security you’ll want to know what threats you can face when implementing AI technologies. prompt injection, information disclosure, data/model poisoning and vector database/RAG Poisoning are some examples. Make sure your AI application and security team are aware of threats.
You can learn more about these threats and ways to address them through the OWASP GenAI Security Project.
Supporting Compliance and Accountability
Depending on your use case there may be some rules you need to abide by. Know what these requirements are and how you can ensure your business and employees follow those rules. Data Privacy, Confidentiality, Geolocation and Vendor Processing are some areas you may need to worry about when deploying an AI solution. The NIST Cybersecurity, Privacy, and AI resources provide additional guidance for managing cybersecurity and privacy risks as AI adoption expands.
Ensure that you can track usage of data and hold someone accountable if misuse occurs.
Applying AI Data Security When Designing Your Application
Make sure your users and executives know that when they are using your AI technology they’re not putting their data at risk. Having good AI data security controls in place can build that trust and allow your employees to use AI safely.
Common Risks When Working with AI Data
Risk 1: Gathering Data for Your Model
There are many things to consider when pulling data for your model. Maybe you don’t know all of the data your gathering contains sensitive information. There may even be malware on some of your data. Ensure you have processes in place when pulling data and only pulling the data you need for your task.
You’ll want to know if the data is safe to use. Apply AI data security best practices when developing your data pipeline. Maybe masking certain data or implementing controls when working with the data in dev/staging environments.
Risk 2: Training and Fine-Tuning Models
Make sure you have proper safeguards in place when you’re training or fine-tuning a model. Data can be tampered with or used for malicious means. Similar to gathering data for your model you’ll want to make sure only approved data is being utilized. Ensure only certain users can gain access to this data as well.
Know where your data is coming from and store that metadata for future reference.
Risk 3: Prompting Your AI Model
Once your model is ready and in production, users will start interacting with it. There can be data leakage from your users if they put in too much information into a prompt. Or perhaps your users are sending sensitive information to AI applications they shouldn’t have access to. Be aware of what information users can share with your model and make sure you’re training them on that as well.
Risk 4: Retrieval-Augmented Generation and Enterprise Search
Similar to risk #3, there are other methods you can leak sensitive information. Utilizing vector databases and connecting AI to your application can expose sensitive information to your users. Make sure you’re applying user permissions to your search services and not allowing anyone to retrieve information they shouldn’t have access to. There could also be prompt injection occurring in your documents so make sure you know what you’re retrieving.
Risk 5: AI Agents and Connected Tools
There are going to be many different tools you can utilize with AI agents. You may allow your agent to access files on your machine or even query your database. Know what actions your agent is capable of performing and what data they can access. If you allow your agent to do anything on your machine you run the risk of leaking more information than what you may want. Similar to RAG attacks there could be a user or documents instructing your agent to take unwanted actions.
Ensure you have proper controls and logging in place to monitor your agents’ activities.
Risk 6: Production and Disposal
As mentioned earlier when an AI application is put into production there are still many things to monitor and watch out for. You could have leaked credentials in your logs or permissions that are no longer being applied properly. Ensure you’re monitoring for any vulnerabilities and have an incident response plan in place if something were to occur.
Also have a disposal process for when you no longer need an AI application.
Core Controls for a Strong AI Data Security Framework
There are many controls you can apply when developing an AI data security Framework. Here are some best practices that we’ve outlined above that can help keep your data safe.
Data Discovery and Classification
Knowing where your data lives is important. You should know where all of your sensitive data is located and have the proper classifications on those files/folders/databases etc. Once you know where your data lives you can implement proper controls around it. Some of your AI applications may have better detection tools to help discover data. Just remember that any AI generated results need to be verified and used to drive some security measure within your environment.
Identity and Least-Privilege Access
Just like any other system you’re working with. Only allow your users to have access to what they need. You can achieve this with the help of IAM technology and enforcing some policies around user access. MFA, Managed Workforce Identity and PAM are some ways you can allow users to be safe and still leverage your AI technology.
Also apply data permissions to your applications and ensure your model isn’t being allowed more data than it needs to.
Encrypt Your Data
Data in transit and data at rest should both be encrypted when dealing with AI data security. Know what data you’re encrypting and be careful with storing your secrets and passwords that are tied to your applications. Also similar to least privilege apply encryption to files you don’t want easily accessible to others.
Secure AI Applications and Dependencies
There can be vulnerabilities with any piece of your AI application. Know what data is being sent to each of those dependencies and make sure they’re keeping it safe. This will also apply when you’re leveraging any AI-generated responses.
Monitor and Respond to Incidents
As with any security implementation, monitoring and responding to incidents is important. You may need to monitor who is accessing what data and what kind of data is being fed into your model. If you have a data breach you want to know who and what was impacted. Depending on your use case you may need to record users IDs, what data source they pulled from and what version of model/application they are using. Make sure to have proper log retention policies in place as well.
A Practical Roadmap for Implementing AI Data Security
There are many steps you can follow to implement AI data security. Here are some that will help you get started. Microsoft also provides guidance on how to build a strong security posture for AI.
Inventory Your AI Assets
Know what AI technologies are currently being used and allowlisted. Know what models/data are being utilized and who has access to them. Create a data flow diagram if possible to map out how your data moves through these AI applications. Once you have that in place, prioritize your list and begin focusing on those that have more sensitive data or are high risk.
Output: AI Asset Inventory/Data Flow
Assess Risk and Build Security Policies
Now that you have your AI assets inventoried and data flows mapped out start digging deeper into each use case. What type of data will be fed into it, will users be sending data to it, is it public facing? What can it do, is it leveraging any external tools? What type of actions can be taken? Once you dig deeper into your data and AI assets build out a risk assessment and create security policies that can help you secure those data flows.
Output: Risk Assessment and Data Security Policies per high risk use-case
Implement Data Security Controls
After building your risk assessments and security policies it’s time to implement controls. Apply data classification, least privilege and encrypt your data. There may be additional controls that you want to put in place for your specific use case as well. Make sure your Data is flowing correctly into your AI applications. Also vet your vendors and make sure they have controls in place for your data as well.
Output: Data is Secure
Test Against Known Threats
Start building out some threat scenarios and apply them to your AI use cases. Make sure you know how to detect each threat and have proper remediation steps if they’re found. Similar to your normal Application Testing process your going to want to test these on an ongoing basis and after key changes. Such as new prompts, models, data or tools being integrated.
Output: Knowledge on Threats and Remediation Steps
Monitor and Respond to Threats
As mentioned above monitor your AI use cases for any signs of threats. Once you know the threats you’re monitoring for build out processes to respond to those threats. You’ll want to incorporate this into your current incident response workflow and make sure your logs are being captured correctly. Remember that there can be sensitive data inside your logs as well.
Output: Monitoring Process for Threat Detection
Reassess Your Risk
Continuously re-assess the risk of your AI use cases. Data flows may have changed since you implemented your controls. Are there new vendors or ways that your data is being used? Ensure you continue to monitor and have processes in place to remediate issues that arise. Keep track of your metrics and know where you’re vulnerable.
Output: Metric Tracking and Improved Risk Posture
How Hexaware Can Help Secure Enterprise AI
As you’ve learned above, there are many aspects to keeping your data secure when implementing AI into your business. Read Hexaware’s AI-Driven Cybersecurity Solutions Aligned with NIST CSF 2.0 for guidance on data protection, monitoring, governance, and incident response.
If you’re looking for ways to implement AI into your application, read AI Guardrails: Autonomous Governance for AI-Powered Development for guidance on privacy, compliance, auditability, and human oversight.
From our perspective this can be done in many ways and we believe that your security strategy should fit your needs. There are various products that can accomplish these goals and many different methods to monitor and secure your Data. Contact us today to learn more on how we can help with your AI data security.
Conclusion
Ensuring AI data security will help keep your business data safe when leveraging AI. As we can see there are many ways to secure your data and prevent it from being leaked or misused. Start by knowing your data and building proper controls to keep it safe. Apply these controls when you’re feeding data into your models or having your AI agents operate on it.
