As organizations accelerate their adoption of artificial intelligence, a critical question emerges: how do you secure systems that behave differently every time you interact with them? Traditional security testing was built for deterministic software where the same input produces the same output. AI systems operate in an entirely different paradigm, generating probabilistic responses that can be manipulated in ways traditional cybersecurity teams never anticipated.
The stakes are significant. According to Adversa AI's 2025 security report, 35% of real-world AI security incidents were caused by simple prompts, with some leading to losses exceeding $100,000 per incident. When OpenAI released GPT-5 in January 2026, red teams from SPLX jailbroke it within 24 hours, declaring it "nearly unusable for enterprise out of the box."
This guide provides security professionals with a comprehensive framework for understanding and implementing AI red teaming. Whether you are a SOC leader extending your team's capabilities, a CISO building a business case for investment, or a security architect evaluating AI security programs, you will find actionable guidance grounded in the latest frameworks, tools, and real-world evidence.
AI red teaming is the practice of adversarial testing specifically designed for AI systems to identify vulnerabilities, safety issues, and security gaps before attackers exploit them. Unlike traditional red teaming that focuses on infrastructure and applications, AI red teaming targets the unique attack surfaces of machine learning models, including training data, inference pipelines, prompts, and model behavior itself.
The practice evolved from military and cybersecurity red teaming traditions but addresses challenges unique to AI systems. Where conventional software behaves deterministically, AI systems produce variable outputs based on probabilistic models. This fundamental difference requires testing approaches that account for statistical variation and emergent behaviors.
According to Growth Market Reports, the AI Red Teaming Services market reached $1.43 billion in 2024 and is projected to grow to $4.8 billion by 2029 at a 28.6% compound annual growth rate. This growth reflects increasing enterprise AI adoption coupled with regulatory pressure from frameworks like the EU AI Act.
Georgetown CSET's research provides essential clarity on what AI red teaming actually encompasses. The term has been applied to everything from prompt hacking to comprehensive security assessments, but effective programs address both the security dimension (protecting AI from malicious actors) and the safety dimension (preventing AI from causing harm).
Organizations implementing AI security programs must understand this dual nature. A system that resists prompt injection but produces biased outputs still poses significant risk. Conversely, a system with strong safety guardrails but weak security controls remains vulnerable to determined attackers.
The distinction between AI safety and AI security testing represents one of the most important conceptual frameworks in AI red teaming.
AI safety testing focuses on protecting the world from AI. This includes testing for:
AI security testing focuses on protecting AI from the world. This includes testing for:
Anthropic's methodology documentation demonstrates how leading AI labs integrate both dimensions. Their red teaming programs employ domain-specific experts (including trust and safety specialists, national security experts, and multilingual testers) to probe both safety and security vulnerabilities.
Effective AI red teaming programs address both dimensions because attackers exploit whichever weakness provides the easiest path. A safety bypass that allows harmful content generation can become a security issue when weaponized. A security vulnerability that exfiltrates training data has safety implications for privacy and trust.
The behavioral threat detection capabilities that security teams deploy for traditional threats must evolve to account for these AI-specific attack patterns.
Effective AI red teaming follows a structured methodology that adapts traditional security testing to the unique characteristics of AI systems.
The AI red teaming process:
Microsoft's AI Red Team documentation provides authoritative guidance on this methodology. Their team developed PyRIT (Python Risk Identification Tool for generative AI) to operationalize these steps at scale.
The scoping phase requires particular attention for AI systems. Unlike traditional applications with defined functionality, AI systems exhibit emergent behaviors that may not be apparent during design. Effective scoping identifies the AI system's intended use cases, the data it accesses, the actions it can take, and the potential impact of failures.
Adversarial strategy development maps potential attack vectors to the specific AI system under test. An LLM-powered customer service chatbot faces different threats than an autonomous AI agent with tool access. The strategy should prioritize attacks based on likelihood and potential impact.
Execution approaches vary based on testing objectives. Discovery testing identifies what vulnerabilities exist. Exploitation testing determines whether vulnerabilities can be weaponized. Escalation testing explores whether initial access can lead to broader compromise. Persistence testing examines whether attackers can maintain access over time.
Reporting and analysis must include reproducible test cases. AI systems produce variable outputs, so test documentation should capture the exact inputs, model versions, and conditions that triggered vulnerabilities. This enables developers to reproduce and fix issues.
The debate between manual and automated AI red teaming has largely resolved into consensus around hybrid approaches.
Manual testing remains essential for discovering novel vulnerabilities. Human creativity identifies attack patterns that automated tools cannot anticipate. According to arXiv research, roleplay attacks achieve 89.6% success rates, logic trap attacks reach 81.4%, and encoding tricks succeed 76.2% of the time. These techniques require human insight to develop and refine.
Automated testing provides scale and systematic coverage. Tools can test thousands of attack variants across model versions, identifying regressions and ensuring consistent security baselines. Giskard's GOAT research demonstrates that automated multi-turn attacks achieve 97% jailbreak success on smaller models within five conversation turns.
Microsoft recommends completing manual red teaming first before implementing automated scaling. Manual testing identifies the attack patterns that matter for a specific system. Automated testing then ensures those patterns and their variants are consistently tested as the system evolves.
Hybrid human-in-the-loop approaches combine both strengths. Automated tools generate candidate attacks based on learned patterns. Human experts review results, identify promising directions, and guide automated exploration toward high-value targets.
For organizations building threat hunting capabilities, this hybrid model mirrors the evolution of network security. Automated detection handles known patterns at scale, while human analysts investigate novel threats.
Traditional red teaming skills provide a foundation for AI red teaming, but the unique characteristics of AI systems require additional capabilities and different approaches.
Table 1: Traditional red teaming vs AI red teaming comparison
This table compares key dimensions of traditional cybersecurity red teaming with AI-specific red teaming, highlighting the expanded scope and different techniques required for AI systems.
The probabilistic nature of AI systems fundamentally changes testing methodology. When a traditional application has a SQL injection vulnerability, it fails consistently to malformed input. When an LLM has a jailbreak vulnerability, it may resist some attempts while succumbing to others. Red teams must run multiple test iterations and report statistical success rates rather than binary pass/fail results.
Attack surfaces differ significantly. Traditional red teams target authentication systems, privilege escalation paths, and network segmentation. AI red teams target these plus model-specific vectors including prompt injection, training data poisoning, and model inversion attacks that extract sensitive information from model outputs.
The skill requirements reflect this expanded scope. Effective AI red teamers combine traditional security expertise with machine learning knowledge and domain expertise relevant to the AI system's use case. According to HiddenLayer's framework, this combination is rare, contributing to talent shortages in the field.
The relationship between AI red teaming and penetration testing causes frequent confusion. Zscaler's comparison framework helps clarify the distinction.
Penetration testing focuses on infrastructure, applications, and network vulnerabilities. Penetration testers attempt to exploit known vulnerability classes in defined scope. The goal is to identify and prioritize remediation of specific security weaknesses.
AI red teaming extends beyond infrastructure to include model behavior, training integrity, and AI-specific attack vectors. AI red teamers attempt to cause the AI system to behave in unintended ways, which may or may not involve exploiting infrastructure vulnerabilities.
Organizations need both for comprehensive security. A well-secured infrastructure does not protect against prompt injection attacks that manipulate model behavior. Conversely, robust model guardrails do not help if attackers can access training data through infrastructure vulnerabilities.
Consider a financial services AI chatbot. Penetration testing would assess the web application hosting the chatbot, the APIs connecting it to backend systems, and the authentication mechanisms protecting it. AI red teaming would assess whether the chatbot can be manipulated to reveal customer data, provide financial advice outside its intended scope, or generate harmful content.
For teams experienced in red team operations, AI red teaming represents an expansion of scope rather than a replacement of existing skills.
AI red teams test for attack categories that differ significantly from traditional security vulnerabilities. Understanding this taxonomy helps practitioners prioritize testing and communicate findings effectively.
Table 2: AI red teaming attack taxonomy
This table catalogs the primary attack categories that AI red teams test for, providing descriptions, examples, and potential impacts to help practitioners understand and prioritize testing efforts.
Prompt injection represents the most prevalent and dangerous AI-specific attack vector. These attacks manipulate AI behavior through crafted inputs, causing systems to execute unintended actions.
Direct injection occurs when attacker-controlled input directly manipulates model behavior. An attacker might submit text that overrides the system prompt, changing the AI's persona, objectives, or constraints.
Indirect injection embeds malicious instructions in external data sources that the AI processes. Tenable's research on ChatGPT vulnerabilities documented indirect prompt injections through SearchGPT reading malicious blog comments, demonstrating how AI systems that consume external content become vulnerable to third-party attacks.
The 2025 Adversa AI report found that 35% of real-world AI security incidents resulted from simple prompt attacks. These attacks require no special tools or expertise, making them accessible to opportunistic attackers.
Effective testing for prompt injection requires creativity in attack formulation and systematic coverage of injection points. Every input the AI system accepts represents a potential injection vector.
Jailbreaking techniques circumvent safety guardrails built into AI systems. Research demonstrates that even sophisticated guardrails fail against determined attackers.
Roleplay attacks achieve 89.6% success rates according to arXiv research. By framing requests within fictional scenarios, attackers convince models to generate content they would otherwise refuse.
Multi-turn jailbreaking builds gradually toward harmful outputs. Giskard's GOAT research shows these attacks achieve 97% success on smaller models and 88% on GPT-4-Turbo within five conversation turns.
Logic trap attacks exploit model reasoning capabilities, achieving 81.4% success rates. These attacks present scenarios where the logically consistent response requires violating safety guidelines.
The speed of jailbreak development underscores the challenge. When OpenAI released GPT-5 in January 2026, red teams jailbroke it within 24 hours, following a pattern seen with Grok-4 and other major model releases.
Testing for jailbreaks requires ongoing effort as both attacks and defenses evolve. A model that resists known jailbreaks today may fall to novel techniques tomorrow.
The rise of autonomous AI agents introduces attack categories that did not exist in traditional LLM security. OWASP's Top 10 for Agentic Applications provides the first dedicated security framework for these systems.
Agent goal hijack (ASI01) redirects an agent's core mission through manipulation. Unlike simple prompt injection, goal hijacking targets the agent's persistent objectives rather than individual responses.
Tool misuse and exploitation (ASI02) causes agents to invoke tools in unintended, harmful ways. Agents with access to email, databases, or external APIs can be manipulated into taking actions their designers never intended.
Identity and privilege abuse (ASI03) exploits agent identities or excessive permissions. Agents often operate with elevated privileges to accomplish their tasks, creating opportunities for insider threats when compromised.
Cascading failures (ASI08) occur when small errors trigger destructive chain reactions across interconnected agent systems. Multi-agent architectures amplify failure modes.
Organizations deploying agentic AI must understand that traditional security controls may not address these attack vectors. Identity threat detection and response capabilities must evolve to monitor AI agent identities alongside human and service account identities.
Testing agentic systems requires evaluating the full scope of agent capabilities, including tool access, memory persistence, and inter-agent communication channels. The attack surface expands with each capability the agent possesses.
Data exfiltration attacks against AI systems may exploit any of these vectors, as agents with broad access can be manipulated into collecting and transmitting sensitive data. Lateral movement patterns in AI environments may look different from traditional network lateral movement, as compromised agents pivot through API connections rather than network paths.
The AI red teaming tool ecosystem has matured significantly, with both open-source and commercial options available for practitioners.
Table 3: AI red teaming tool comparison
This table compares major open-source AI red teaming tools, highlighting their developers, strengths, key features, and licensing to help practitioners select appropriate solutions.
Microsoft's PyRIT has emerged as the leading enterprise tool. It integrates with Azure AI Foundry and includes the AI Red Teaming Agent released in April 2025 for automated testing workflows. PyRIT's attack library covers prompt injection, jailbreaking, and content safety testing.
NVIDIA's Garak focuses on LLM vulnerability scanning with an extensive probe library. Version 0.14.0 is currently in development with enhanced support for agentic AI systems. Garak's plugin architecture enables custom probe development for organization-specific requirements.
Red AI Range provides a Docker-based environment for simulating AI vulnerabilities, making it valuable for training and educational purposes.
Commercial platforms from Zscaler, Mindgard, and HackerOne offer managed services and additional capabilities for organizations preferring vendor support. These typically include compliance reporting, continuous testing integration, and expert consultation.
Selecting the right tool requires matching capabilities to organizational needs.
PyRIT strengths include Microsoft backing, comprehensive documentation, and deep Azure integration. Organizations using Azure AI services benefit from native support. The attack library reflects Microsoft's AI Red Team experience testing production systems including Bing Chat and Microsoft 365 Copilot.
Garak strengths include NVIDIA's AI expertise, focus on LLM probing, and extensive vulnerability detection capabilities. The tool excels at systematic testing across multiple models and identifying regressions between versions.
Selection criteria should include:
For security operations center teams building AI red teaming capabilities, these tools complement rather than replace human expertise. Automated tools provide coverage and consistency. Human testers provide creativity and novel attack development.
Threat detection feeds into tool configuration as new attack techniques emerge. Organizations should establish processes for updating attack libraries based on emerging threats and vulnerability disclosures.
AI red teaming operates within an evolving landscape of frameworks and regulations. Understanding these requirements helps organizations structure effective programs and demonstrate compliance.
Table 4: AI red teaming framework crosswalk
This table maps major AI governance frameworks to their red teaming requirements, helping organizations understand the regulatory landscape and align testing programs with compliance obligations.
NIST's AI Risk Management Framework positions adversarial testing as part of the Measure function. The framework defines red teaming as "an approach consisting of adversarial testing of AI systems under stress conditions to seek out AI system failure modes or vulnerabilities."
MITRE ATLAS extends the ATT&CK framework for AI-specific threats. The October 2025 update added 14 new techniques focused on AI agents and generative AI systems. ATLAS now includes 15 tactics, 66 techniques, 46 sub-techniques, 26 mitigations, and 33 case studies.
OWASP provides multiple resources including the Top 10 for LLM Applications (2025 version), the Gen AI Red Teaming Guide released January 2025, and the Top 10 for Agentic Applications released December 2025.
For organizations navigating compliance requirements, these frameworks provide authoritative guidance that satisfies regulatory expectations and demonstrates due diligence.
The EU AI Act introduces mandatory requirements for adversarial testing of high-risk AI systems. Promptfoo's EU AI Act guidance details the specific obligations.
High-risk classification determines whether AI red teaming is mandatory. Systems in areas including critical infrastructure, education, employment, law enforcement, and border control face heightened requirements.
Documentation requirements include adversarial testing as part of the risk management system. Organizations must demonstrate that they have identified and mitigated potential vulnerabilities through systematic testing.
Timeline: Full compliance for high-risk AI systems is required by August 2, 2026. General-purpose AI (GPAI) models with systemic risk face additional red teaming obligations.
Penalties for non-compliance reach up to 35 million EUR or 7% of global annual turnover, whichever is higher.
Organizations deploying AI in European markets must integrate red teaming into their compliance programs. Even organizations outside the EU may face requirements if their AI systems affect EU citizens.
MITRE ATLAS provides the taxonomy that AI red teams use to structure testing and report findings.
Framework structure mirrors ATT&CK's familiar format. Tactics represent adversary goals. Techniques describe how adversaries achieve those goals. Mitigations provide defensive recommendations.
AI-specific tactics include:
AML.TA0004 - ML Model Access: Techniques for gaining access to machine learning modelsAML.TA0012 - ML Attack Staging: Techniques for preparing attacks against ML systemsOctober 2025 update added 14 new techniques addressing AI agents and generative AI, developed in collaboration with Zenity Labs.
Integration with red team findings provides consistent reporting. When red teams discover vulnerabilities, mapping them to ATLAS techniques enables comparison across assessments and tracking of remediation progress.
For teams familiar with MITRE ATT&CK, ATLAS provides a natural extension for AI systems. The frameworks share conceptual foundations while addressing different attack surfaces.
Establishing AI red teaming capabilities requires deliberate investment in people, processes, and tools. This section provides practical guidance for organizations at various stages of maturity.
Team composition for AI red teaming spans multiple disciplines:
According to AI Career Finder, AI Red Team Specialist salaries range from $130,000 to $220,000, with demand growing 55% year over year. The talent shortage means organizations often build hybrid teams combining internal security expertise with external AI specialists.
Implementation phases follow a maturity model:
Build vs buy decisions depend on organizational context. Internal teams provide deep institutional knowledge and ongoing capability. Managed services from MDR providers offer expertise without hiring challenges. Hybrid approaches engage external specialists for novel testing while building internal capability.
Building a business case for AI red teaming requires quantifying both costs and benefits.
Cost benchmarks from Obsidian Security indicate external AI red teaming engagements start at $16,000 or more depending on scope and complexity. Internal teams require salary investment plus tools, training, and ongoing development.
Efficiency gains demonstrate measurable return. Organizations with mature AI red teaming programs report 60% fewer AI-related security incidents. This translates to reduced incident response costs, fewer business disruptions, and avoided regulatory penalties.
Risk avoidance justification centers on prevented losses. The Adversa AI report documents that simple prompt attacks have caused losses exceeding $100,000 per incident. A single prevented incident can justify substantial program investment.
Justification framework should address:
Point-in-time assessments provide snapshots but miss the dynamic nature of AI systems. Continuous red teaming addresses this limitation.
Why continuous: AI models evolve through fine-tuning, prompt engineering changes, and underlying model updates. New attack techniques emerge constantly. Defenses require ongoing validation. A system that passed testing last quarter may have new vulnerabilities today.
Integration with CI/CD: Automated red teaming tools can execute in development pipelines, testing each model update before deployment. This catches regressions early and prevents vulnerable changes from reaching production.
Testing cadence recommendations:
Monitoring and alerting complement testing by identifying exploitation attempts in production. Behavioral analysis can detect anomalous AI system behavior that may indicate ongoing attacks.
The AI red teaming landscape continues to evolve rapidly, with new approaches emerging to address the expanding AI attack surface.
Automated continuous testing has moved from experimental to mainstream. Platforms like Virtue AI's AgentSuite provide continuous red teaming using over 100 proprietary agent-specific attack strategies across 30+ sandbox environments. According to Help Net Security, this addresses a critical gap: IBM reports that 79% of enterprises are deploying AI agents, yet 97% lack proper security controls.
Multimodal testing extends beyond text to image, voice, and video inputs. As AI systems accept richer inputs, attack surfaces expand. Voice cloning attacks have demonstrated the ability to bypass multi-factor authentication through social engineering.
Agentic AI focus dominates current investment. The OWASP Top 10 for Agentic Applications released in December 2025 codifies the threat landscape for autonomous agents. Testing these systems requires evaluating tool access, memory persistence, and inter-agent communication.
AI-assisted red teaming uses AI systems to generate adversarial inputs at scale. This approach discovers attack patterns humans might miss while raising questions about AI systems testing AI systems.
Industry consolidation reflects market maturation. CrowdStrike's acquisition of SGNL for $740 million addresses AI identity authorization. Palo Alto Networks acquired Chronosphere for AI observability. These deals signal that AI security has become a strategic priority for major cybersecurity solutions vendors.
NVIDIA's sandboxing guidance emphasizes that containment is the only scalable solution for agentic AI workflows. Their AI Red Team recommends treating all LLM-generated code as untrusted output requiring sandboxed execution.
Vectra AI approaches AI security through the lens of assume compromise and Attack Signal Intelligence. Rather than relying solely on prevention, effective AI security programs must combine proactive red teaming with continuous monitoring and detection.
This means testing AI systems adversarially while simultaneously maintaining visibility into how those systems behave in production. The goal is identifying anomalous patterns that might indicate exploitation and responding rapidly when attacks succeed.
Resilience, not just prevention, defines security maturity for AI systems. Organizations using the Vectra AI platform extend detection and response capabilities to cover AI-related threats alongside traditional network, identity, and cloud attack patterns.
Network detection and response capabilities provide visibility into AI system communications, identifying data exfiltration attempts, command and control patterns, and lateral movement that involves AI infrastructure.
The AI red teaming landscape will continue evolving rapidly over the next 12 to 24 months. Security professionals should prepare for several key developments.
Agentic AI proliferation will drive new attack categories. As organizations deploy AI agents with increasing autonomy and tool access, the attack surface expands dramatically. The OWASP Agentic Top 10 represents the beginning of framework development for these systems. Expect additional guidance, tools, and regulatory attention focused specifically on autonomous agents.
Regulatory convergence will shape compliance requirements. The EU AI Act sets the most prescriptive requirements, but other jurisdictions are developing their own frameworks. Organizations operating globally will need to reconcile potentially conflicting requirements while maintaining effective security programs.
Multimodal attacks will become more sophisticated. Current red teaming focuses heavily on text-based attacks against LLMs. As AI systems process images, audio, video, and sensor data, attack techniques will target these modalities. Voice deepfake attacks have already demonstrated effectiveness against authentication systems.
AI-on-AI security raises new questions. When AI systems defend against AI-powered attacks, the dynamics differ from human-versus-machine scenarios. Red teams will need to evaluate how defensive AI systems perform against adversarial AI rather than just human attackers.
Investment priorities should include:
Organizations should track MITRE ATLAS updates, OWASP framework releases, and emerging CVEs in AI infrastructure components. The field moves quickly, and today's best practices may become insufficient as threats evolve.
AI security learning resources from Vectra AI provide ongoing guidance as the landscape evolves.
AI red teaming is the practice of adversarial testing specifically designed for AI systems to identify vulnerabilities, safety issues, and security gaps before attackers exploit them. Unlike traditional red teaming that focuses on network and application security, AI red teaming targets the unique attack surfaces of machine learning models including training data, inference pipelines, prompts, and model behavior itself.
The practice combines security testing (protecting AI from malicious actors) and safety testing (preventing AI from causing harm). Effective programs address both dimensions because attackers exploit whichever weakness provides the easiest path to their objectives. AI red teams use specialized tools, techniques, and frameworks like MITRE ATLAS and the OWASP Top 10 for LLMs to structure their testing methodologies.
AI red teaming differs from traditional red teaming in several fundamental ways. Traditional red teaming targets deterministic systems where the same input produces the same output. AI systems are probabilistic, producing variable outputs that require statistical analysis across multiple test iterations.
The attack surface expands significantly. Traditional red teams target networks, applications, and infrastructure. AI red teams target these plus model-specific vectors including prompt injection, training data poisoning, jailbreaking, and model evasion. This requires different skills combining traditional security expertise with machine learning knowledge.
Testing frequency also differs. Traditional red teaming often occurs annually or quarterly. AI systems require continuous testing because models evolve, new attacks emerge constantly, and defenses need ongoing validation.
The primary open-source tools for AI red teaming include Microsoft's PyRIT, NVIDIA's Garak, DeepTeam, and Promptfoo. PyRIT integrates with Azure AI Foundry and includes a comprehensive attack library reflecting Microsoft's experience testing production systems. Garak focuses on LLM vulnerability scanning with an extensive probe library and plugin architecture.
Commercial platforms from Zscaler, Mindgard, and HackerOne offer managed services with compliance reporting and expert consultation. Red AI Range provides a Docker-based environment for training and vulnerability simulation.
Tool selection depends on the AI systems being tested, team expertise, integration requirements, and priority threat scenarios. Most organizations use multiple tools in combination with manual testing.
AI safety testing focuses on protecting the world from AI. This includes testing for bias and discrimination, hallucinations and factual errors, harmful content generation, and potential for misuse. The goal is ensuring AI systems behave as intended and do not cause harm to users or society.
AI security testing focuses on protecting AI from the world. This includes testing for prompt injection attacks, data exfiltration, model manipulation, and unauthorized access. The goal is preventing malicious actors from exploiting AI systems.
Comprehensive AI red teaming programs address both dimensions. A safety bypass can become a security issue when weaponized. A security vulnerability has safety implications when it affects user privacy or enables harmful outputs. Understanding incident response procedures becomes critical when AI systems are compromised.
Prompt injection is an attack technique where malicious inputs manipulate AI model behavior. Direct injection occurs when attacker-controlled input directly overrides system instructions, changing the AI's persona, objectives, or constraints.
Indirect injection embeds malicious instructions in external data sources the AI processes. For example, an AI that reads web content might encounter malicious instructions hidden in blog comments or web pages, executing those instructions as if they came from legitimate users.
According to 2025 research, 35% of real-world AI security incidents resulted from simple prompt attacks. Testing for prompt injection requires creativity in attack formulation and systematic coverage of all inputs the AI system accepts.
The EU AI Act requires adversarial testing for high-risk AI systems as part of conformity assessment before market deployment. Organizations must demonstrate that they have identified and mitigated potential vulnerabilities through systematic testing and document this testing as part of their risk management system.
High-risk classifications include AI systems in critical infrastructure, education, employment, law enforcement, and border control. Full compliance is required by August 2, 2026. General-purpose AI models with systemic risk face additional red teaming obligations.
Penalties for non-compliance reach up to 35 million EUR or 7% of global annual turnover. Organizations deploying AI in European markets should integrate red teaming into their compliance programs now.
MITRE ATLAS provides the taxonomy that AI red teams use to structure testing and report findings. The framework extends MITRE ATT&CK for AI-specific threats, including 15 tactics, 66 techniques, 46 sub-techniques, 26 mitigations, and 33 case studies.
The October 2025 update added 14 new techniques addressing AI agents and generative AI systems. AI-specific tactics include ML Model Access (`AML.TA0004`) and ML Attack Staging (AML.TA0012).
Mapping red team findings to ATLAS techniques enables consistent reporting, comparison across assessments, and tracking of remediation progress. Organizations familiar with ATT&CK will find ATLAS provides a natural extension for AI security.
No. While tools like PyRIT, Garak, and commercial platforms enable automated testing at scale, manual expert testing remains essential for discovering novel vulnerabilities. Automated tools excel at systematic coverage and regression testing but cannot match human creativity in developing new attack techniques.
Microsoft recommends completing manual red teaming before implementing automated scaling. Manual testing identifies the attack patterns that matter for a specific system. Automated testing then ensures those patterns are consistently tested as the system evolves.
The most effective approaches combine human creativity with automated efficiency through human-in-the-loop methodologies where automated tools generate candidate attacks and human experts guide exploration.