Stress-Testing AI: Proactive Defense with Red Teaming

News & Resources
Stress-Testing AI with Red Teaming

Publish Date

May 28, 2024



Adversarial Testing | Red Teaming | Security

Artificial intelligence (AI) might be an unstoppable train, but we can take concrete steps to keep it on track. While AI enhances patient outcomes, detects fraud, and personalizes recommendations, it also poses risks if not properly managed. We don’t want AI spilling our organization’s secrets or aiding bad actors. As the great philosopher Uncle Ben said to Peter Parker, “With great power comes great responsibility.”

AI is being adopted at an unprecedented pace, and your reaction might be to yell, ‘slow down!’ But screaming at a machine is not an effective mitigation strategy. Red teaming, on the other hand, very much is.

Red teaming is simply stress testing your AI. It involves pretending to be a bad guy to identify and address weaknesses before they can be exploited by an actual bad guy. Deploying AI without red teaming is like letting the train run at full speed without a conductor at the controls.

“There is no better way to understand how a real attack will play out than to employ the same tactics, techniques, and procedures used by adversaries.” – George Kurtz, CEO CrowdStrike

For decision-makers, the stakes are high. AI systems often handle sensitive personal information, financial records, and proprietary business insights. A breach or misuse of this data can damage both your reputation and your bottom line. The average cost of a data breach is $4.45 million. That’s not a risk any of us can afford.

As you navigate the AI landscape, whether implementing custom solutions or integrating pre-packaged tools, prioritizing red teaming is not a “nice to have” – it’s a must. By proactively identifying and addressing vulnerabilities, red teaming helps build safer, more robust, and trustworthy systems.

In this article, we’ll explore the critical role of red teaming in two common AI scenarios:

  1. Customizing open-source AI with proprietary data
  2. Deploying off the shelf AI tools into your existing tenant

We’ll dive into the unique challenges and best practices associated with each, arming you with the knowledge to make informed decisions and safeguard your organization’s AI investments.

By embracing this essential practice, you’ll position your organization at the forefront of responsible AI adoption, ensuring that your AI systems are not just powerful but also secure, reliable, and trustworthy.

Understanding AI Red Teaming

AI red teaming puts your system through a set of rigorous tests. It’s designed to spot weaknesses by thinking like a hacker or an adversary and exploiting those weaknesses. You’re pushing it to its limits to ensure it performs well no matter what curveballs are thrown its way.

At the core of red teaming is adversarial testing, which challenges the AI’s decision-making with tricky, unexpected inputs. This not only tests the AI’s ability to handle surprises but also improves its accuracy and reliability, making sure it can stand up to real-world challenges.

Adversarial testing is crucial for several reasons. It enhances security by identifying and correcting vulnerabilities that could allow AI manipulation, ensuring the system’s outputs remain trustworthy even under adversarial conditions. It also promotes safety by uncovering how AI systems might fail when confronted with erroneous or malicious input, preventing errors that could have serious implications, particularly in critical applications. Furthermore, adversarial testing builds trust by demonstrating resilience against attacks, reassuring stakeholders of the AI’s reliability, and enhancing its credibility – which fosters wider adoption.

Amazon had to abandon its AI-driven recruiting engine after discovering it was biased against women. The algorithm was trained on resumes submitted predominantly by men, leading to biased hiring recommendations.

Adversarial testing is vital wherever AI decisions influence outcomes – from logistics to customer service to decisions like lending or hiring. This testing helps maintain operational continuity and secures the trust of users, making it an essential practice for any organization leveraging AI technology.

Red Teaming Custom AI Solutions

Implementing a customized open-source AI platform with proprietary data offers enormous advantages, but also unique security and integrity challenges. These systems are often tailored to process sensitive and valuable information, which can include Personally Identifiable Information (PII), strategic company insights, or other data you don’t want the wrong people to access. The primary challenge is ensuring the AI does not inadvertently expose or misuse this data.

Adversarial testing in this scenario involves ensuring data integrity by maintaining the accuracy and confidentiality of data, even when presented with misleading or corrupted inputs. It also examines response robustness, testing how the system handles extreme, unexpected, or out-of-pattern data to uncover potential vulnerabilities. This includes simulating real-world attacks to mimic potential threats like unauthorized data access or manipulated decision-making processes.

To effectively red team custom AI solutions, consider the following best practices:

  • Regular Vulnerability Assessments: Conduct these assessments frequently to catch new vulnerabilities that could emerge as the system evolves.
  • Collaborative Red Teaming: Engage different groups within your organization, including those who may think differently about security, to participate in red teaming exercises. This diversity of thought often uncovers additional flaws.
  • Transparent Documentation: Maintain detailed records of red teaming exercises, outcomes, and remedial actions. This not only helps in refining the security measures but also builds trust among stakeholders by showing commitment to transparency and accountability.

Imagine a large organization using AI to streamline internal processes. Red teaming in this scenario could simulate various employees trying to obtain HR (human resources), financial, and other confidential information. Through these exercises, the company could identify data that is not properly secured, allowing them to adjust the security levels on documents, folders, and data.

Red Teaming Off-the-Shelf AI Platforms

AI tools like Microsoft’s Copilot, OpenAI’s ChatGPT, and Google’s Gemini are amazing because of their plug-and-play capabilities. They allow you to rapidly integrate advanced AI functionality into your organization in a significantly more cost-effective way than custom solutions. To be effective, these tools typically train on all your organization’s information, including that extremely sensitive information, you really don’t want the wrong people seeing. This makes securing that data a top priority.

For these AI tools, adversarial testing encompasses several strategies. Monitoring inputs and outputs helps identify any unexpected or inappropriate behavior by regularly checking the data the AI processes and its responses. Role-based access testing simulates attempts to interact with the AI using different user roles, ensuring only authorized personnel can access sensitive data or critical functions. In addition, scenario-based testing involves creating tests that mimic real-life situations the AI might face, assessing its ability to handle complex, sensitive, or ambiguous data effectively.

To effectively red team off the shelf AI solutions, consider the following best practices:

  • Continuous Monitoring: Unlike custom systems where changes are internally controlled, pre-packaged AI solutions require ongoing monitoring to manage updates or changes made by the vendor.
  • Know Your Tool: Work closely with AI vendors or a Managed Service Provider (MSP) to understand the security measures that are in place out of the box and stay up to date as the security environment evolves.
  • Incident Response Planning: Prepare for potential security breaches or failures by having a robust incident response strategy ready to go should information be exposed.

Imagine a retail company using an AI customer service bot. Red teaming in this scenario could simulate various customer interaction scenarios to test the AI’s handling of confidential customer information. Through these exercises, the company could find potential data leakage points or inappropriate data handling, allowing them to adjust the AI’s protocols and settings to better protect customer information.

Key Takeaways for Stress-Testing AI

Red teaming is not just an option but a necessity in the evolving landscape of AI deployment. Whether you’re customizing an open-source AI platform with proprietary data or integrating pre-packaged AI tools, the security and integrity of your systems depend on rigorous stress-testing and proactive vulnerability management. Three key takeaways include:

  1. Red Teaming Is Critical: Adversarial testing is at the heart of red teaming, challenging AI systems with complex, unexpected inputs to ensure they can handle real-world threats. This process is crucial for finding and mitigating potential vulnerabilities, enhancing the security, safety, and trustworthiness of AI deployments.
  2. Custom AI Solutions Offer More Flexibility but Require More Safeguards: When implementing customized AI solutions, focus on data integrity checks, response robustness, and real-world attack simulations. Regular vulnerability assessments, collaborative red teaming efforts, and transparent documentation are essential best practices to maintain the integrity and security of these systems.
  3. Off-the-Shelf AI Platforms Require Extensive Permission Audits: For off-the-shelf AI platforms, the main challenges include data privacy, black box operations, and dependency on vendor security practices. Effective red teaming involves continuous monitoring, input and output analysis, role-based access tests, and scenario-based testing. Collaborate closely with vendors and prepare robust incident response plans to manage potential breaches.

Red teaming is an ongoing commitment, not a one-time exercise. By continuously challenging and improving your AI systems, you ensure they remain robust and secure as threats change and evolve. Embracing this essential practice will help you build powerful, reliable, and trustworthy AI systems, securing your organization’s future in the AI-driven world.

So, are you ready to put your AI to the test? Start implementing red teaming practices today or find a provider who can help ensure you and your systems are protected. Your data, your reputation, and your business depend on it. If you’re not sure where to begin, let’s start a conversation: reach out to today to get started.


About the Author

Greg Starling, Head of Emerging Technologies at Doyon Technology Group

Greg Starling serves as the Head of Emerging Technology for Doyon Technology Group. He has been a thought leader for the past twenty years, focusing on technology trends, and has contributed to published articles in Forbes, Wired, Inc., Mashable, and Entrepreneur magazines. He holds multiple patents and has been twice named as Innovator of the Year by the Journal Record. Greg also runs one of the largest AI information communities worldwide.

Doyon Technology Group (DTG), a subsidiary of Doyon, Limited, was established in 2023 in Anchorage, Alaska to manage the Doyon portfolio of technology companies: Arctic Information Technology (Arctic IT®), Arctic IT Government Solutions, and designDATA. DTG companies offer a variety of technology services including managed services, cybersecurity, and professional software implementations and support for cloud business applications.