Artificial intelligence (AI) has come a long way over the last few years. Now, it’s thoroughly embedded within the IT infrastructure of most businesses. Chances are, your employees already use AI to draft documents, summarise data, and make crucial decisions.
But this unprecedented productivity comes at a cost. In this case, the trade-off for greater speed and efficiency is an increased level of risk. When AI has access to everything, so does anyone who manages to breach it.
Where do these AI security risks come from? And what can you do to mitigate them? That’s what this article will explore.
AI and Security Risks: Why Your Technology Might be Dangerous
Data Exposure
The most fundamental rule of AI technology (especially large language models, or LLMs) is that it requires data in order to function properly. Unfortunately, this dependency creates an enormous risk factor. The more data your AI tools can access, the worse the damage will be if just one of them is compromised. Your internal defences may be the strongest available, but this means nothing if threat actors manage to breach a third-party platform.
Model Manipulation Attacks
AI systems are vulnerable to brand-new types of cyber-attack. Model manipulation is just one example. This is when threat actors (from inside or outside the company) actively meddle with your AI solutions to cause harm. They might poison the instructions it’s given, or the data it uses to make decisions. Prompt injection attacks are a particularly big concern.
AI-Augmented Threats
You’re not the only ones leveraging AI to make your jobs easier. Threat actors are doing exactly the same thing. Deepfake technology is making phishing scams more convincing than ever. As early as 2024, CNN was already reporting major attacks that implemented AI. As the technology continues to improve, this will only get worse.
Shadow AI and Ungoverned Tool Adoption
Shadow AI – the unsanctioned use of AI by individuals within your company – is a less obvious risk factor, but still one to consider. Those participating may not even realise they’re putting the business in danger. Situations like this are more likely to occur when AI use is not properly governed and controlled.
The Importance of AI Governance
To businesses who have been happily implementing new tools without a care in the world, “AI governance” might just sound like more red tape designed to slow them down. This is far from the truth. Governance protects the company from several angles:
- Strengthening Security: The first and most obvious benefit of governance is that it reduces your risk of experiencing a data breach, by controlling how AI can be used and enforcing certain controls.
- Improving Regulatory Compliance: While data protection laws may not have caught up to AI quite yet, they will eventually. The earlier you get to work, the less likely you are to be audited later.
- Ensuring Accountability: A clear set of rules makes it far easier to keep everyone accountable and responsible. This limits the risk of misuse.
- Building Stronger Relationships: Jumping on the AI governance bandwagon early makes your business look responsible and trustworthy, helping you build stronger partnerships with clients and vendors.
Performing an AI Security Risk Assessment
An AI security risk assessment will help you locate and address vulnerabilities that could be quietly putting your entire business at risk. Perform one by following these steps:
Step 1: Catalogue Tools
Start by cataloguing your AI tools. Document which solutions your business currently uses, what they do, and how much access they have to your data. Each tool will have a unique risk profile, so it’s important not to skip this step.
Step 2: Assess Data Risk
Identify which types of data each platform is able to view and process. Categorise information based on sensitivity. Important questions to ask include:
- Is personal information being entered into the tool?
- Is data sent to external servers for processing, and if so, where are those servers located?
- Does the vendor use customer data to train or improve its models?
- Which data retention policies apply, and do they align with your regulatory requirements?
Step 3: Review the Vendor's Security Posture
The vendor’s security practices matter just as much as yours, especially if there’s any chance that they’re collecting sensitive data. Review each vendor’s privacy policy and data processing agreement carefully. If you find any that aren’t implementing sufficient controls, consider cutting or replacing that tool.
Step 4: Check AI-Generated Output
It’s important to have a very clear picture of the output your AI solutions are generating, especially if it’s used in client-facing documents or considered in major decisions. Here, it might be helpful to speak to those actually using the platform on a daily basis. Get an idea of how well each model follows instructions, and whether it’s putting out usable content. During this stage, you should also check which human review processes are in place.
Step 5: Document Findings and Define Controls
Record your findings and note down any gaps you’ve uncovered during your analysis. Then, decide which controls and policies will best address them. You might need to strengthen rules around human oversight, for instance, or restrict the types of data AI tools are allowed to use.
Remember to document this entire process. Not only will it help your team remember the new policies, but it may also be useful if your business gets audited.
Step 6: Review Periodically
AI technology is in a state of flux, so it’s crucial to recognise that any changes you implement now may be meaningless a year or two down the road. Repeat the AI security risk assessment process:
- At least once per year
- After any major changes occur within your IT infrastructure
- Immediately after experiencing a breach
The Role of an AI Governance Framework
An AI governance framework will help streamline and speed up the risk assessment process, by providing a set of guidelines your business will follow every single time. All you need to do is compare your existing environment against the framework. Generally, it should cover:
- Approved Tool Inventory: Which kinds of AI solutions your business is allowed (or not allowed) to implement.
- Data Use: Which types of data an AI tool is allowed to access, use, and store.
- Vendor Assessment Criteria: Which requirements your AI vendors will be held to.
- Security Controls: Which security measures (e.g. access controls or encryption) will be used to reduce risk.
- Monitoring and Audit Capability: How you will verify that AI tools are being used correctly and are not introducing unnecessary risk. Include documentation practices here, as well.
You can either build your own framework, or base it on an existing one. Either way, some customisation may be required to ensure that it fully addresses your needs.
Read more: 4 Easy Tips to Increase Security in Microsoft 365
Make Sure AI Doesn’t Become Another Threat
AI is a valuable tool, but it can also introduce a variety of security risks if you’re not careful. The best way to ensure that doesn’t happen is by implementing strict governance structures. The earlier you can accomplish this, the better. By introducing some simple rules and controls now, you’ll be able to enjoy the benefits AI provides while dramatically reducing risk.
Need help identifying the gaps in your security posture? Atarix will take care of that. Our experts dig deep into your digital infrastructure, uncovering vulnerabilities you didn’t even know existed. Get a comprehensive audit today.
