AI Red Teaming Explained: Why Modern Enterprises Need it Now

What Is AI Red Teaming?

AI red teaming broadens the traditional playbook to include adversarial testing of AI models—ranging from data pipelines to prompt engineering and model APIs. Security isn’t strictly about model accuracy and fairness; it also includes misalignment risks, manipulated responses, and potential regulatory pitfalls (e.g., EU AI Act). By scrutinizing how models behave under malicious input, organizations can identify weaknesses before attackers capitalize on them.

Unique Attack Surfaces in AI Systems

When organizations integrate AI models into their workflows, a new attack surface emerges. Methods like prompt injection, data poisoning, or model theft can compromise trust. Multimodal AI systems that juggle text, images, and other data streams are especially complex. Attackers may aim for “jailbreaking” LLMs or stealthily altering training data. With these systems closely tied to enterprise apps and public APIs, the landscape grows more perilous if defenders leave the metaphorical “window” open.

Why AI Red Teaming Is Critical

AI has become integral to many business processes, from fraud detection to customer support chatbots. As enterprises deploy LLMs and AI agents, threat actors see new opportunities to exploit untested edges of these technologies. Moreover, recent regulations—such as the US Executive Order on AI—amplify the importance of robust security practices. For CIOs and CISOs, ignoring AI vulnerabilities is not an option. Proactive red teaming fosters resilience and protects reputations in a world where innovation can outpace defenses.

Key Differences Between AI Red Teaming and Web Application Penetration Testing

Web application penetration testing traditionally focuses on discovering vulnerabilities across websites, APIs, and core network layers—from injection flaws like SQLi or XSS to identifying misconfigured servers. AI red teaming, by contrast, scrutinizes model behavior, data pipelines, and prompt integrity, opening the door to a completely different set of threats such as adversarial prompt injection and data poisoning.

It simulates real-world adversarial interactions to uncover:

• Security vulnerabilities (e.g., prompt injection, data exfiltration, model manipulation)
• Safety risks (e.g., harmful or biased output, policy violations)
• Reliability failures (e.g., hallucination under stress, brittleness at edge cases)
• Alignment gaps (e.g., outputs that conflict with enterprise policy or regulatory requirements)

AI red teaming has become a central requirement in frameworks such as the NIST AI Risk Management Framework, the White House Executive Order on Safe AI, and global standards guiding enterprise AI governance.

Focus and Attack Surface

Traditional web security focuses primarily on safeguarding networks, infrastructure, and user credentials. In contrast, AI security shifts the emphasis to model behavior, data pipelines, prompt integrity, and coordination among multiple agents. Therefore, while web application testing targets vulnerabilities like SQL injection or cross-site scripting, AI red teaming investigates risks tied to data poisoning, malicious prompt injection, and emergent behaviors driven by large language models.

Methodologies and Tools

Conventional web penetration testers rely on familiar suites (e.g., Burp Suite, Metasploit), social engineering scripts, and recognized exploitation frameworks. AI red teams, on the other hand, need specialized toolkits that include adversarial prompt libraries, “jailbreak” techniques, and continuous automation to probe model responses. While both practices can reference frameworks like MITRE ATT&CK, AI red teams often look to adversarial ML–specific guidelines, many of which are still nascent and evolving alongside novel AI capabilities.

Outcome and Remediation Differences

Web application testing typically results in patch cycles, firewall rule updates, and improved security information and event management (SIEM) configurations. AI remediation can be more nuanced: it may involve retraining an entire model, cleansing or augmenting data sets, or refactoring prompts to mitigate emergent misbehaviors. Because AI models and their behaviors continuously evolve, organizations need ongoing AI red teaming rather than periodic, one-time assessments.

Business and Governance Implications

Standards like ISO 27001 and NIST CSF address many dimensions of cybersecurity but do not deeply interrogate AI concepts such as model bias or content moderation. Regulatory mandates like the EU AI Act and the White House Executive Order on Safe AI introduce higher levels of oversight, budgetary investment, and cross-functional coordination. For CIOs and CISOs, integrating web application security practices with AI-specific controls is now essential to maintain both compliance and robust enterprise governance.

Why AI Red Teaming Requires a New Approach

Modern AI tools aren’t simply software components wrapped in an API; they’re sophisticated generative systems that respond to—and learn from—real-time user input. As a result, the nature of potential exploits and vulnerabilities diverges sharply from traditional network or web application security paradigms.

• LLMs and generative AI systems behave erratically: Complex language models can produce off-topic, incorrect, or harmful content when presented with unusual prompts.
• The attack surface is conversational and highly manipulable: Subtle phrasing tweaks in a conversation thread can force unexpected behaviors or “jailbreak” a model’s guardrails.
• AI systems can be exploited without breaching the network: Because attackers can manipulate prompts rather than hack systems, enterprise firewalls can’t protect a model from accepting malicious input.

Overall, these realities demand a new security lens for AI-centric threats. Conventional tests for SQL injection or DDoS attacks are insufficient in an environment where the language model itself can be deceived. By adopting this new red teaming approach, organizations stay one step ahead of adversaries intent on subtle yet damaging AI manipulations.

Key Risks AI Red Teaming Identifies

Security Risks

Adversaries might exploit AI models through prompt injection and “jailbreaking” to override content filters or reveal model-internal data. Attackers can also orchestrate data exfiltration through seemingly benign prompts, effectively turning the AI into an unwitting conduit for leaking internal information. AI security testing helps surface these vulnerabilities before malicious actors can exploit them.

Safety Risks

LLMs and multimodal AI can produce harmful recommendations, exhibit bias, or generate toxic and disallowed content under adversarial pressure. Attackers can also steer AI outputs toward social engineering or misinformation, further amplifying reputational damage. AI red teaming highlights these risks by aggressively testing for policy violations and unsafe language generation.

Reliability and Performance Risks

Even large, well-trained AI models sometimes “hallucinate” facts or produce inconsistent outputs. Under stress or when faced with edge-case scenarios, these models may fail to deliver accurate or safe responses. Red teaming scenarios push AI systems to their limits, illuminating potential reliability gaps and stress-induced breakdowns.

Core Components of an AI Red Teaming Program

Threat Modeling for AI Systems

Threat modeling is the foundation of an AI red team exercise. Teams identify critical assets—such as model architectures, training data, embeddings, and vector databases—as well as potential adversaries (ranging from external hackers to rogue insiders). There is also rising concern about agentic AI harms, where a misaligned AI might take unexpected actions on its own.

Attack Simulation and Vulnerability Discovery

AI red teams employ adversarial tactics to expose vulnerabilities. They use automated jailbreaking tools and specialized code to test a model’s responsiveness to malicious prompts or manipulative data inputs. By generating stress and edge-case scenarios at scale, they systematically uncover weaknesses in prompt handling, model logic, and data governance.

Safety and Alignment Testing

Since LLMs can deliver toxic, biased, or policy-violating outputs, safety and alignment testing is crucial. AI red teams measure how well toxicity filters stand up to adversarial prompts. They also look for unintentional bias and ensure that generated content adheres to compliance and content moderation standards.

Secure AI Development Lifecycle Integration

True AI security comes from continuous oversight embedded in development pipelines. AI red teaming activities ideally occur both pre-launch and post-launch, with the outputs feeding back into iterative improvement. Through integration with CI/CD pipelines, enterprises ensure that any new model updates, data sources, or feature expansions undergo the same rigorous security testing.

Why Do You Need AI Red Teaming? Uses Cases for Enterprise AI Security

Adopting AI in critical functions—finance, healthcare, real-time customer support—sets off the first alarm for implementing AI red teaming. Handling sensitive data further raises the stakes. If you suspect your traditional security posture has gaps in regard to AI-driven systems, that’s a prime indicator it’s time to act. Regulatory pressure also plays a defining role, especially if your organization is bound by stringent rules on data privacy or algorithmic accountability.

Evaluating LLM-Powered Business Applications

IT helpdesk copilots, customer support chatbots, and AI-driven employee productivity suites represent prime targets for adversarial manipulation. These systems handle large volumes of user data and can accidentally leak sensitive or incorrect information. By proactively scanning for exploitable prompts, AI red teaming ensures these applications maintain integrity and compliance. Thorough evaluations also pave the way for safer AI deployments across an enterprise’s digital ecosystem.

Securing Agentic AI Systems

Agentic AI systems can execute workflows autonomously, potentially working across multiple business apps. They might take actions that are not logged or fully visible to human supervisors.

AI agents can:

• Take actions across apps
• Execute workflows autonomously
• Access sensitive business systems

One misconfigured model could initiate severe compliance or operational risks.

AI red teaming tests for:

• Unauthorized actions
• Privilege escalations
• Chain-of-thought manipulation
• Tool usage vulnerabilities

Vendor & Supply Chain Evaluation

Organizations increasingly inherit AI risk from external sources, amplifying the importance of thorough vetting.

Organizations increasingly inherit AI risk from:

• SaaS platforms
• Third-party chatbots
• Embedded AI features

Without proper evaluations, a single compromised third-party AI feature could disrupt an entire operation.

How to Build an AI Red Teaming Framework

To build a resilient approach to AI security, organizations should take deliberate, strategic steps at every stage. The following actions help ensure safe deployment and ongoing protection against evolving threats:

• Establish executive governance: Ensure AI security is driven by senior leadership, who own strategy, budget, and reporting. Their involvement aligns red team insights with enterprise risk thresholds, fosters cross-functional collaboration, and embeds AI security into broader organizational initiatives.
• Define AI boundaries: Use Zscaler DSPM and DLP to identify sensitive data locations and set strict guardrails on AI access, preventing PHI/PII leakage in prompts. Defining what data AIs touch is critical for safe model deployment across diverse data sources.
• Employ red team methodologies and tools: Combine manual techniques and automated scans—like prompt injection and adversarial input simulations—to identify AI vulnerabilities. A hybrid approach ensures both classic and emerging threats are revealed, strengthening the overall security posture against manipulation.
• Report and analyze findings: Thoroughly document vulnerabilities, test scenarios, and potential business impacts. Clear, prioritized analysis helps stakeholders focus remediation efforts on high-risk areas, translating findings into actionable steps for improving the organization's AI security.
• Mitigate and retest: Address discovered weaknesses through model retraining, data cleanup, and updated policies. Ongoing retesting is vital, as evolving AI systems can introduce new risks. Regular validation keeps security proactive and ensures remediation efforts are effective.

Practical Considerations & Best Practices

Given that AI red teaming calls for an interdisciplinary approach—security professionals collaborating with data scientists—organizations often face resource constraints. Focus on:

• Scoping for multi-step, agent-driven attacks—especially for LLM-based services.
• Measuring success by tracking discovered vulnerabilities, response times, model improvements, and fairness metrics.
• Establishing enterprise governance by aligning red team findings with established risk management frameworks to ensure that the board understands and supports ongoing AI security initiatives.

Common Pitfalls and Limitations of AI Red Teaming 

Although AI red teaming can uncover deep-seated vulnerabilities, it is no cure-all. Pinning all hopes on red teams alone, without strengthening overall security and addressing issues like data hygiene, is a misstep. Professionals with expertise in both ML and security are still rare, so the talent pool is limited. Models evolve, and new attack vectors can surface before teams are aware, leading to a perpetual chase.

Emerging Trends in AI Red Teaming

The adoption of automated, continuous testing is surging. Open source solutions like AutoRedTeamer and BlackIce can script adversarial interactions at scale, probing for memory exploits or injection flaws with minimal human oversight. Over time, expect more synergy between traditional and AI-centric red teaming, yielding specialized sub-disciplines within cybersecurity.

Planning for the Future

Forward-thinking CIOs and CISOs are adopting roadmaps that evolve their cybersecurity teams into AI-ready ones. This strategy may involve hiring professionals proficient in adversarial ML, establishing data governance committees, and aligning with compliance frameworks like the NIST AI RMF. The future is about uniting tried-and-true security principles with advanced ML-specific defenses.

Partner with Zscaler to Secure the Entire AI Lifecycle

Zscaler, building on its leadership in zero trust security, has joined forces with SPLX to deliver end-to-end AI security that spans from initial development through full-scale enterprise deployment, closing the gap between traditional and AI-centric risk management. As part of this collaboration, SPLX has launched the market-first Policy Generator—a feature that enables AI Security teams to automatically create on-domain, fine-tuned runtime protection policies for Zscaler AI Guard, AWS Bedrock guardrails, and more. By directly embedding findings from simulated AI red teaming assessments within the platform, Policy Generator streamlines the connection between red and blue teaming, accelerates time to market, and eliminates the need for teams to build guardrail policies from scratch.

Customers now benefit from a unified platform that delivers:

• Comprehensive AI asset discovery and risk assessment across models, workflows, and data pipelines
• Automated AI red teaming and dynamic remediation with over 5,000 attack simulations and real-time vulnerability fixes
• Advanced runtime guardrails and prompt hardening to prevent prompt injection, data leakage, and malicious behaviors
• Streamlined AI governance and compliance alignment with evolving standards and global regulations

Ready to see how Zscaler and SPLX can secure your organization’s AI lifecycle? Request a demo today.