AI Red Teaming as a Blueprint for Proactive Product Security

AI has significantly expanded red teaming capabilities, introducing new attack surfaces for models and the products and systems they power.  It’s even reached the White House.

Executive Order (EO 14110), issued in October 2023, introduced a federal framework for AI’s safe and secure development, including mandates for red teaming to uncover safety, bias, and misuse risks. Red teaming is no longer a niche practice – it’s now a strategic requirement across AI development lifecycles.

Microsoft’s AI Red Team (AIRT) conducted security evaluations on over 100 GenAI systems, identifying vulnerabilities with implications far beyond tech. These insights are especially critical for regulated, connected sectors like healthcare, automotive, and critical infrastructure, where model behavior can affect safety, compliance, and reputation.

Are the GenAI systems integrated into your workflows, introducing silent risks?
This article highlights three essential takeaways from Microsoft’s research that product security leaders can use to strengthen their AI red teaming strategies, particularly in domains where safety, regulation, and trust intersect.

AI Red Teaming ≠ Zero Risk

AI tools and systems are susceptible to prompt injection, jailbreaking, and data poisoning attacks. A separate study found that 20% of GenAI jailbreak attempts were successful. Researchers also noted that it took attackers an average of 42 seconds and 5 interactions to succeed.  

*Image taken from the MSFT report 

In a connected healthcare ecosystem, attackers can leverage a Vision-Language Model (VLM) to jailbreak and compromise medical devices by altering diagnostic outputs or modifying patient records. This image-to-text prompt attack poses serious risks to patient safety while exposing sensitive PII and violating data privacy laws.

Other sectors, such as the automotive or industrial, may face heightened supply chain security risks with a software update containing malicious dependencies that a pre-trained AI model might have mistaken for a legitimate package from a trusted source. An independent study analyzing 576,000 code samples generated by 16 large language models (LLMs) found that nearly 20% of package dependencies referenced by these models did not exist. The study also concluded that open-source models produced ~22% more hallucinated dependencies than commercial alternatives.

Package hallucinations can escalate into breaches without proper validation or AI security guardrails. This risk is directly tied to red teaming exercises, which simulate real-world attacks mapped to adversarial tactics, techniques, and procedures (TTPs) to identify how automated development workflows might unintentionally introduce malicious dependencies into the software supply chain.

3 Valuable Lessons for Security Teams in Regulated or High-Impact Domains

Here are several key takeaways and lessons uncovered by Microsoft’s AI Red Team (AIRT):

  • Lesson 1: AI security extends beyond code
    AI red teaming risks exist in workflows, integrations, and third-party dependencies. AI red teaming must be supervised by skilled subject matter experts (SMEs), as certain domains require human judgment, particularly regarding safety decisions. SMEs must also validate automated workflows to ensure that AI-generated logic is accurately evaluated before anything gets pushed to production. This includes reviewing infrastructure-as-code templates and deployment scripts for hallucinated dependencies and other security risks. 
  • Lesson 2: Context is everything
    AI systems rely on a deep understanding of contextual risk, related to specific business objectives, practical experience, and the ability to validate AI-generated outputs. Product security teams must test AI systems for critical vulnerabilities, business logic flaws, and threat scenarios unique to their environment. For example, a medical device manufacturer (MDM) should simulate jailbreaking attacks during red team exercises on imaging devices to assess whether an attacker could manipulate diagnostic outputs or alter scan results, leading to misdiagnosis or patient safety issues, and potentially triggering costly product recalls.
  • Lesson 3: Red team insights should feed into the product lifecycle
    Red teaming insights must be designed to fit the product lifecycle. AI systems utilizing retrieval-augmented generation (RAG) architectures are often vulnerable to cross-prompt injection attacks (XPIA), which can manipulate LLMs into following incorrect instructions and ultimately produce harmful or unauthorized outputs that compromise system integrity and security. 

Other best practices product security teams can implement with AI red teaming include:

  • Audit LLM outputs for sensitive content, hallucinations, or excessive permissions
  • Map adversarial threats to real-world attack paths (e.g., malicious dependency installation in automotive)
  • Validate third-party model integrations for data leakage
  • Red team for ethical compliance, not just technical flaws

C2A Security: Closing the Loop Between Red Teaming, Risk, and Compliance

AI red teaming reveals real risks – but what happens next?
Teams need a platform that connects red team outcomes to risk scoring, design controls, and product release decisions to turn findings into secure, compliant, and shippable products.

C2A Security’s EVSec platform provides that layer of intelligence.

EVSec enables product security teams to:

  • Simulate and tag red team findings on a live cyber model, visualizing the product architecture, tied to actual systems and components.
  • Trace threat paths across the SDLC, from LLM-generated logic to SBOM violations and regulatory controls.
  • Log red teaming results into centralized risk scoring and compliance evidence.
  • Drive remediation through contextual triage, team workflows, and regulatory alignment.
  • Continuously improve security posture via post-incident review loops and model-level threat refinement.

AI red teaming isn’t just a model-hardening practice – it’s a strategic imperative for regulated, connected industries. But red team insights are only valuable if they’re operationalized.

C2A Security’s EVSec platform ensures your red teaming outcomes don’t stay trapped in spreadsheets or static reports – but become actionable, measurable inputs into your product security and compliance programs.

Schedule a demo to learn how C2A Security can help complement your AI red teaming strategy.