Guide: AI Security

Foundations of AI Security

As organizations move quickly to integrate AI into their organization, significant AI security threats and sensitive data exposure are introduced. Organizations must move fast in their transition to becoming AI-ready. However, this transition needs to include data security to account for AI security issues so that these models are used and governed properly. 

The Basics of AI Security

Few innovations have shaken the world of data security more in the past few years than AI. Like it or not, AI is here to stay, and companies that still don’t have an AI security strategy risk exposing themselves to new and emerging threats. Organizations that use large language models (LLM) and machine learning (ML) consume large amounts of data. However, actually understanding what is going on within these models is difficult to determine, even by experts. The opaque nature of AI means that it is difficult to ensure that sensitive data, both consumed and produced, are not exposed to AI security risks.  

What is AI security?

AI security can mean many things, but it generally refers to a category of technologies that use machine learning and artificial intelligence to augment threat detection and response. It’s not about replacing people but making it easier for them to stay ahead of the latest threats. AI data security may also refer to protecting AI systems themselves. For instance, LLMs and other AI models need protection against threats like data poisoning attacks.

On a basic level, artificial intelligence security systems are trained to identify potentially malicious behaviors at a speed and scale that simply aren’t possible with manual processes. In this sense, they amplify the capabilities of their human counterparts, acting as a force multiplier for security initiatives.

AI can also assist with many other routine security processes, such as aggregating alerts and automating responses. To that end, it can support the entire threat lifecycle, from detecting a potential threat to remediation. And, given the speed at which AI works compared to people, it’s proven vital in adopting a more proactive stance to security. 

Key elements of AI security

While the topic of AI security is very broad, there are some key requirements for it. These include:

  1. Data governance: Ensuring that AI and LLMs are governed by dynamic policies that protect sensitive data. This requires that security policies are dynamic and manage how AI interacts with internal data, including redacting sensitive information when necessary. 
  2. Compliance: Meeting compliance requirements is necessary. This not only reduces the stress of audits and creating audit reports, the time and effort needed for compliance tasks, and the likelihood of non-compliance. Compliance requirements such as the EU AI Act are still relatively new and evolving, and it is important to stay on top of these changes and requirements. 
  3. Access Management: Controlling access to sensitive data, especially given the large volume of data AI and LLMs consume, so that these teams can remain productive while still limiting access to sensitive information. This is further complicated as these models often consume data across a number of different ML and business intelligence (BI) stacks. Managing access and automating this process is necessary for AI protection.
  4. Privacy Policy Management: Locating sensitive data is a critical component. Given the amount of data, the days of relying on manual processes are over. It is necessary to automatically and continuously classify and tag sensitive data to dynamically implement security and privacy policies, ensuring data is always protected. These policies must scale access permissions of AI apps and products to your data. 
  5. Visibility and Activity Monitoring: Given the opaque nature of AI and LLMs, monitoring user access to AI and ML systems to prevent unauthorized access and exposure of sensitive data is especially important. This requires that you maintain detailed audit logs of all data interactions within your AI security and complete visibility of what users send and receive from LLMs. This is crucial to your organization’s remaining in control of AI use. 
  6. Fast AI Reliability: Balancing the need for rapid AI development and deployment with the necessity of managing data privacy and security risks effectively.

These measures form the foundation of artificial intelligence security, which is necessary to ensure that sensitive data is secured so that organizations can leverage AI effectively without compromising on data security and compliance. 

Is your business ready for AI technologies?

Although there’s no denying the value of secure AI, the reality is that most businesses aren’t ready. Your business needs AI reliability, which requires a strategic approach that involves preparing your technical infrastructure for AI security solutions.

This begins with modernizing and consolidating your data architecture. The average business now uses over 130 SaaS applications – and that’s on top of any in-house priority technologies they might be using, including AI systems themselves. Every one of these digital touchpoints is a potential source of risk. If you don’t have complete visibility and control over all of them, then you end up with multiple single points of failure.

As organizations grow, data inevitably ends up being spread across multiple databases, data warehouses, and data lakes. When that happens, manual access becomes difficult and time-consuming. Attempting to layer on AI onto such an environment means that AI, just like people, will be unable to make informed decisions. In other words – garbage in, garbage out.

To get your data in order, you need to review your information governance program and make sure it’s ready for AI. That means consolidating your various data sources under a single pane of glass – a centralized dashboard that lets you manage access, policies, and auditing for all of your data. Once you have a single source of truth (SSoT), you’ll be ready to layer on AI in a way that’s sustainable and reliable.

Understanding the AI advantage in security

There are countless potential business use cases for AI, and security is no exception. In fact, AI models themselves must be protected from threats like data poisoning and violations of privacy and security policies. It’s essential that all AI models and their usage are properly governed, and AI itself plays a central role in making that possible at the required scale.

AI model security also goes hand in hand with governance and compliance. For example, once you know exactly where your sensitive data lives, you can create and apply access and usage policies. Then, AI can continuously monitor your data architecture to detect unusual patterns that could point to a potential attack or risk of an accidental data leak.

When implementing custom LLMs, you can layer on AI to enforce dynamic security policies. For instance, these policies might dictate that certain types of data must be redacted before it can be used for training the model. By understanding the underlying context in addition to the primary data, AI can detect potential threats and policy breaches in real time with an aptitude as good as or better than a human analyst.

With a complete data security platform like Satori, you can consolidate access, security, and compliance under a single interface. This also brings together your underlying data assets, including production databases, data warehouses, and data lakes. The end result is a solution that lets your security analysts and decision-makers do their best work at a speed that wouldn’t be possible to attain manually.  

Don’t get left behind in the race to protect your business against the next generation of cyber threats. Book your Satori demo now to see first-hand how our AI-powered solutions can help you regain control over your information security and governance.

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