For years, red teaming has helped organizations find security gaps before criminals do. But discovering vulnerabilities is only half the equation. While companies routinely test their networks and also deploy firewalls to stop attacks in real-time, most AI security programs focus on either testing or protection, not both. This fragmented approach becomes increasingly dangerous as AI handles sensitive operations across the enterprise.
The problem isn’t that security teams don’t understand AI risks. They know that prompt injection can compromise chatbots and that jailbreaking can bypass safety controls. What they lack are comprehensive strategies that combine proactive vulnerability discovery with runtime defense. Organizations need both red teaming to find weaknesses before deployment AND next-generation AI firewalls to stop attacks in production. Traditional security learned this lesson decades ago. AI security is just catching up.
Why AI Vulnerabilities Require Different Testing
When security teams test traditional systems, they look for specific flaws in predictable locations. SQL injection happens when input validation fails. Cross-site scripting occurs when output encoding is missing. Test once, patch, and the vulnerability is gone. AI models don’t work this way because their weaknesses emerge from how they process language and context, and more importantly, their behavior is probabilistic rather than deterministic.
Run the same prompt through an AI model ten times and you might get ten slightly different responses. This probabilistic nature means testing must be continuous and adversarial, accounting for countless prompt and response scenarios. A model that seems secure today might generate harmful content tomorrow based on subtle changes in input phrasing or context. Unlike patching a code vulnerability that stays fixed, AI vulnerabilities can resurface through creative prompt engineering even after initial testing.
The complexity multiplies when you consider that effective AI testing needs to be conducted by AI itself. Human testers, no matter how skilled, cannot generate the volume and variety of prompts needed to discover edge cases. They can’t adapt their attacks in real-time based on model responses. They can’t think like an AI system to find the non-intuitive vulnerabilities that emerge from transformer architectures and attention mechanisms. Testing AI requires AI-powered approaches that operate at machine speed and scale.
Take a customer service chatbot handling thousands of daily conversations. During training, it learns patterns for helpful responses, but this training creates exploitable behaviors. Through carefully constructed multi-turn conversations, attackers can manipulate the model’s helpful nature to extract training data or generate unauthorized responses. The chatbot isn’t malfunctioning when this happens. It’s following its training in ways developers never anticipated, and these vulnerabilities might only appear under specific conversational contexts that human testers would never think to try.
The Expertise and Scale Challenge
Even organizations committed to AI security testing face practical barriers that make comprehensive validation nearly impossible with current approaches. Security professionals who excel at network and application testing rarely have the prompt engineering skills needed to craft effective AI attacks. The few specialists who understand both domains command premium salaries and typically work for specialized consulting firms rather than individual enterprises.
Manual testing also can’t keep pace with how quickly organizations deploy new models. A thorough security assessment requires thousands of test prompts across multiple attack categories, and that’s just for a single model. Meanwhile, development teams release updates weekly as they fine-tune performance and add capabilities. The testing backlog grows exponentially, forcing security teams to either slow deployment or accept untested models in production.
But here’s what many organizations miss: even perfect testing wouldn’t be enough. Models that pass comprehensive security validation can still be compromised through novel attack techniques discovered after deployment. Attack methods evolve constantly. PyRIT published 200 exploits that anyone can use. Many-shot jailbreaking worked against every major provider until they patched it weeks later. New techniques emerge monthly, and models in production remain vulnerable until the next testing cycle.
Beyond Testing: The Need for AI-Firewall and Runtime Protection
This is where the concept of next-generation AI firewalls becomes essential. Just as network security combines vulnerability scanning with real-time firewalls, AI security requires both pre-deployment testing and runtime protection. An AI firewall sits between users and models, inspecting every interaction for attacks and filtering responses for harmful content. It provides the continuous defense that testing alone cannot deliver.
Consider how this works in practice. Red teaming might discover that a model is vulnerable to role-playing attacks where users pretend to be system administrators. You update the model’s training or add instructions to prevent this. But attackers don’t stop there. They develop variations, combine techniques, or discover entirely new approaches. Without runtime protection, every novel attack succeeds until you discover it, test for it, and retrain the model.
Runtime protection also addresses the reality that not every vulnerability can be fixed through model updates. Some weaknesses are inherent to how language models function. Others would require extensive retraining that could degrade model performance in other areas. An AI firewall provides compensating controls, blocking attacks that exploit these unfixable vulnerabilities while allowing legitimate use to continue.
The combination of testing and protection creates defense-in-depth for AI systems. Testing reduces the attack surface by finding and fixing vulnerabilities before deployment. Runtime protection handles what testing misses, including zero-day attacks and creative variations of known techniques. Together, they provide the comprehensive security that AI systems require.
Building Effective AI Red Team Programs
Organizations serious about AI security need integrated approaches that combine continuous testing with runtime protection. This starts with automated red teaming that operates continuously rather than in periodic assessments. Models need testing during development, before deployment, and continuously in production as attack techniques evolve. The testing must be adversarial, using AI to probe AI systems at scale and speed humans cannot match.
Automation becomes essential not just for scale but for sophistication. AI-powered testing can generate millions of prompt variations, adapt based on model responses, and discover non-obvious vulnerabilities through systematic exploration. It can simulate multi-turn conversations that gradually manipulate model behavior, test multimodal inputs that combine text with images or audio, and identify subtle patterns that indicate exploitable behaviors.
Runtime protection must be equally sophisticated, using AI to detect and block AI attacks in real-time. This means understanding user intent, not just matching keywords. It means recognizing attack patterns across conversation turns, not just individual prompts. It means filtering responses for harmful content that might seem benign out of context but becomes dangerous when combined with earlier interactions.
The Path Forward with Automated Red Teaming
The gap between AI deployment speed and comprehensive security won’t close through hiring or manual processes. Organizations need purpose-built tools that provide both testing and protection at enterprise scale. This is why WitnessAI developed an integrated platform combining Witness Attack for automated red teaming with Witness Protect as a next-generation AI firewall.
Witness Attack uses simulated attack techniques including multimodal attacks, multi-step jailbreaks, comprehensive fuzzing, and reinforcement learning attacks to discover vulnerabilities before deployment. The platform adapts its testing based on what it learns about each model, providing the continuous, adversarial validation that AI systems require. When vulnerabilities are found, it provides specific remediation guidance while automatically updating protection rules.
Witness Protect complements this with a next generation AI-Firewall providing runtime defense that stops attacks in production. It inspects every interaction between users and models, blocking prompt injection, preventing data exfiltration, and filtering harmful responses. The AI firewall provides consistent protection across all models, whether OpenAI, Anthropic, Google, or custom implementations. Together, they create comprehensive security from development through production.
Moving Towards Security Assurance
AI security isn’t optional anymore, and half-measures won’t suffice. Regulators require both testing and protection. Boards expect comprehensive risk management. Attackers exploit any gap between testing cycles or protection coverage. Organizations choosing between testing OR protection are making a false choice. Modern AI security requires both.
The technology exists today to implement comprehensive AI security that combines continuous testing with runtime protection. Organizations that move quickly will deploy AI with confidence, knowing they can both find vulnerabilities proactively and stop attacks reactively. In an environment where AI advantage determines market position, the ability to deploy quickly AND securely becomes the ultimate competitive differentiator.