AI Red Teaming: Proactive Defense for Modern CISOs

AI red teaming tests how AI systems fail under adversarial conditions. Learn core components, frameworks, and best practices for continuous security validation.

What Is an AI Red Team?

At 2:47 AM on a Tuesday, an adversary injected carefully crafted prompts into your organization's AI customer service chatbot. Within minutes, the bot exposed personally identifiable information from its training data: names, email addresses, internal knowledge base entries your security team assumed were protected. Your traditional security tools were never alerted because this wasn't a code vulnerability. This was a prompt injection attack, and your penetration testing methodology missed it entirely because you tested for SQL injection, not natural language manipulation. The breach could have exposed customer records before your team found the anomaly.

AI red teaming extends penetration testing techniques to address how AI systems fail under adversarial conditions, from prompt injection attacks to model manipulation and data poisoning. You're testing two dimensions: how implementation choices create systemic vulnerabilities, and how development practices introduce security risks before deployment.

CISA's 2024 guidance positions AI red teaming as part of third-party safety and security evaluations within established cybersecurity frameworks, building on testing methodologies refined over decades.

Why AI Red Teaming Matters

AI systems introduce failure modes that traditional security testing cannot find. Standard penetration tests evaluate code vulnerabilities, network configurations, and access controls. They miss the behavioral risks in how AI models respond to adversarial inputs, how training data can be poisoned, and how natural language interfaces create entry points for attackers.

The gap between traditional testing and AI-specific risks grows as organizations deploy more AI systems. A customer service chatbot, fraud detection model, and content moderation system each present unique attack opportunities that standard security assessments overlook. Adversaries already exploit these gaps through prompt injection, model manipulation, and data poisoning attacks.

Security teams that rely solely on traditional assessments leave these vulnerabilities unexamined. AI red teaming closes these gaps by systematically testing how AI systems behave under adversarial conditions. To build an effective program, organizations need to understand how this testing connects to existing cybersecurity operations.

Core Objectives of AI Red Teaming

AI red teaming focuses on finding vulnerabilities specific to machine learning systems before adversaries exploit them. The primary goal is validating whether your security controls stop attacks that target AI model behavior, training data integrity, and natural language interfaces.

Effective programs pursue several interconnected objectives:

• Identify AI-specific attack surfaces: Map vulnerabilities in model inputs, training pipelines, and inference endpoints that traditional security assessments miss.
• Validate defensive coverage: Test whether existing security tools find prompt injection attempts, adversarial inputs, and data poisoning indicators.
• Assess model behavior under adversarial conditions: Determine how AI systems respond when attackers manipulate inputs to produce unintended outputs or extract sensitive information.
• Evaluate risks across the AI lifecycle: Examine vulnerabilities introduced during development, deployment, and production operations.
• Measure security control gaps: Quantify which attack techniques your defenses stop versus those requiring remediation.

These objectives extend beyond one-time assessments. Organizations integrating AI red teaming into continuous security operations gain ongoing visibility into how model updates, retraining cycles, and infrastructure changes affect their defensive posture. Understanding these objectives helps security teams build the right team structure and technical capabilities.

Core Components of an AI Red Team

Effective AI red teaming programs center on three automation layers: offensive automation with autonomous red team agents, adversary emulation frameworks enhanced with AI, and CI/CD-driven continuous testing. Practitioners focus on automating adversary emulation campaigns, building intelligent response workflows, and engineering detection-as-code pipelines.

• MITRE ATT&CK framework foundation

The MITRE ATT&CK framework provides your baseline knowledge structure, mapping adversary tactics, techniques, and procedures into systematic testing scenarios. This framework enables consistent evaluation across your organization and collaboration between red and blue teams through shared terminology. When you operationalize ATT&CK testing, you use Atomic Red Team, a collection of scripts mapping directly to ATT&CK techniques. These command-level tests validate whether your security tools identify specific adversarial behaviors.

• Framework integration architecture

Effective programs are built on multiple complementary frameworks: MITRE ATLAS for AI-specific threat taxonomy, NIST AI Risk Management for risk management structure, and OWASP ML Top 10 for AI-specific vulnerabilities. CISA 2024 guidance recommends building upon lessons learned from software security's four-decade evolution of TEVV guidance rather than creating entirely new testing frameworks.

• Multidisciplinary team requirements

The company's AI red team requires expertise spanning traditional security, data science, machine learning security operations, and domain-specific knowledge. Forrester's AI red team guidance emphasizes that no single skillset suffices; you need diverse perspectives to address the complex interplay of technical, operational, and business risks.

Types of AI Red Teaming Activities

AI red teaming encompasses distinct activity categories, each targeting different vulnerabilities in machine learning systems. Security teams select and combine these approaches based on their AI deployment models, risk profiles, and regulatory requirements.

The primary activity types include:

• Prompt injection testing: Craft malicious inputs designed to manipulate AI model outputs, bypass safety controls, or extract information the model should protect. This includes direct injection through user inputs and indirect injection through external data sources the model processes.
• Model evasion attacks: Develop adversarial inputs that cause AI systems to misclassify data or produce incorrect outputs. These attacks test whether small, carefully designed perturbations can fool image recognition, malware classifiers, or fraud detection systems.
• Data poisoning simulations: Assess vulnerabilities in training pipelines by attempting to inject malicious data that would compromise model behavior after retraining. This activity identifies weaknesses in data validation and provenance controls.
• Model extraction attempts: Test whether attackers can reconstruct proprietary models by querying them repeatedly and analyzing outputs. Successful extraction exposes intellectual property and enables adversaries to find additional vulnerabilities offline.
• Training data extraction: Probe models to determine whether they leak sensitive information from their training datasets. Large language models and other AI systems can inadvertently memorize and expose personally identifiable information, credentials, or confidential business data.
• Safety guardrail testing: Attempt to bypass content filters, alignment controls, and output restrictions through jailbreak techniques. This validates whether safety mechanisms hold under creative adversarial pressure.

Organizations typically begin with prompt injection and safety guardrail testing for customer-facing AI applications, then expand to more technical assessments as their programs mature. The specific techniques used within each activity type depend on how the red team structures its operations.

How AI Red Teaming Works

AI red teaming operates across three phases: pre-deployment vulnerability identification during development, development-phase assessment of how implementation choices create systemic vulnerabilities, and post-deployment continuous testing of production systems.

• Detection validation against adversary techniques

Start by validating whether your security platform finds known adversary techniques. SentinelOne's Singularity Platform found all 16 attacks and all 80 steps in MITRE ATT&CK evaluations with zero detection delays, providing baseline validation for your red team operations. This baseline validation tells you whether your deployed security controls provide the coverage your organization assumes exists.

• Adaptive threat simulation

AI-enhanced operations dynamically adjust attack strategies based on your defensive responses. When you block one attack vector, the autonomous red team agent explores alternative techniques mapped to the same adversary objective within the MITRE ATT&CK framework. Automation and adaptive security technologies can find, adapt to, and anticipate security vulnerabilities more effectively than manual-only operations. You gain realistic assessment of whether your security operations can respond to evolving attacks.

• Continuous testing integration

AI red teaming integrates into MLOps and CI/CD workflows, ensuring security testing executes routinely with every model update, retraining, or deployment. SentinelOne's partnerships with Keysight and SafeBreach enable security teams to safely simulate threats and continuously validate that the Singularity Platform is deployed correctly. You automate adversary emulation campaigns, build intelligent response workflows, and engineer detection-as-code pipelines.

• Threat correlation and investigation

SentinelOne's patented Storylines technology automatically correlates endpoint events into complete attack narratives, enabling you to track multi-step attack chains across your infrastructure. This correlation matters because sophisticated attacks span multiple systems and techniques. You validate whether simulated lateral movement, privilege escalation, and data exfiltration sequences are properly found and correlated.

Common Attack Techniques Used in AI Red Teaming

Red teams employ specific technical methods to probe AI system weaknesses. These techniques go beyond identifying vulnerability categories to actively exploiting them through proven attack patterns.

Effective red teams build their arsenals around these core techniques:

• Adversarial perturbations: Introduce subtle modifications to inputs that humans cannot perceive but cause AI models to fail. In image classification, this means altering pixels by small amounts that change model predictions entirely. In text-based systems, this involves character substitutions or homoglyphs that bypass content filters.
• Context manipulation: Structure prompts to shift how the model interprets its role or constraints. Techniques include role-playing scenarios that encourage the model to adopt personas with fewer restrictions, or multi-turn conversations that gradually erode safety boundaries.
• Instruction override: Embed commands within user inputs or external data sources that the model treats as system-level instructions. Attackers hide these directives in documents, web pages, or database entries the AI processes during normal operations.
• Membership inference: Query models systematically to determine whether specific data points were part of the training dataset. Successful inference reveals private information and can expose organizations to regulatory penalties.
• Gradient-based attacks: For white-box assessments where red teams have model access, use gradient information to craft optimally adversarial inputs. These mathematically derived attacks achieve higher success rates than random perturbation methods.
• Transfer attacks: Develop adversarial examples against surrogate models, then apply them to target systems. This technique works because vulnerabilities often transfer between models trained on similar data or architectures.

Red teams document which techniques succeed against specific model types and deployment configurations. This intelligence shapes both immediate remediation priorities and longer-term security architecture decisions.

Risks Identified Through AI Red Teaming

AI red teaming uncovers organizational risks that extend beyond technical vulnerabilities. These exercises reveal how AI system failures translate into business impact, regulatory exposure, and operational disruption.

Red team assessments commonly surface these risk categories:

• Data privacy violations: Models that memorize and expose personally identifiable information, protected health data, or financial records from training datasets create liability under GDPR, HIPAA, and state privacy laws.
• Intellectual property exposure: AI systems trained on proprietary data can leak trade secrets, source code, or confidential business strategies through carefully constructed queries.
• Regulatory compliance failures: AI systems in regulated industries must meet specific accuracy, fairness, and explainability standards. Red teaming identifies where models fall short of requirements from agencies like the FDA, SEC, or banking regulators.
• Reputational damage vectors: Customer-facing AI that generates offensive content, provides dangerous advice, or exhibits bias creates public relations crises that erode brand trust.
• Operational integrity risks: AI systems integrated into critical workflows become single points of failure. Red teaming reveals how adversaries could disrupt operations by manipulating model outputs that drive automated decisions.
• Financial fraud enablement: Fraud detection and transaction monitoring models vulnerable to evasion attacks allow criminals to bypass controls designed to stop money laundering, account takeover, or payment fraud.
• Supply chain vulnerabilities: Third-party models, training data providers, and ML infrastructure introduce risks outside direct organizational control. Red teaming maps these dependencies and their associated exposure.

Quantifying these risks in business terms helps security teams prioritize remediation and communicate findings to executive leadership. The benefits of systematic AI red teaming become clear when organizations understand the full scope of what these assessments protect against.

Key Benefits of AI Red Teaming

AI red teaming enables systematic exploration at scale, testing thousands of input variations, parameter combinations, and attack sequences. These coverage levels would be impossible within manual testing timeframes and budgets, while validating security controls against documented adversary techniques in relevant ATT&CK groups.

• AI-specific vulnerability detection

Traditional penetration testing misses vulnerabilities unique to AI systems. Forrester Research 2024 analysis shows AI red teaming blends offensive security tactics with safety evaluations for bias, toxicity, and reputational harm. This expands security scope beyond code-level exploits. These AI-specific attack vectors require fundamentally different testing methodologies than traditional application security assessments.

• Continuous validation and drift detection

Once implemented, autonomous AI red teaming provides continuous testing capabilities through integration with MLOps and CI/CD workflows. You find security control drift as configurations change, models retrain, or infrastructure updates occur, identifying degraded security posture before adversaries exploit gaps.

• Framework-standardized measurement

Established frameworks enable systematic coverage measurement. You map test results to MITRE ATT&CK techniques, demonstrating to executive leadership which adversary behaviors your security controls find and which require additional investment.

Challenges and Limitations of AI Red Teaming

The field currently lacks established best practices, with Georgetown CSET research documenting through expert workshops that participants generally agreed on the absence of standardized methodologies for adversarial AI testing. Organizations deploying AI red teaming encounter predictable challenges that undermine program effectiveness.

• Narrow focus on model vulnerabilities

Your biggest mistake would be focusing exclusively on model vulnerabilities while overlooking how implementation architectures and sociotechnical systems create exploitable conditions. Current AI red teaming efforts focus predominantly on individual model testing while overlooking broader sociotechnical systems. Research on AI sociotechnical systems reveals that organizations must address emergent behaviors arising from complex interactions between models, users, and environments, not just test isolated model security.

• Novel AI failure classes

Research on AI systems indicates that autonomous agents exhibit new broad classes of failures that exist specifically for AI systems: failures that could compromise safety or security, potentially turning the AI into a malicious insider. These novel failure classes mean your existing pentesting playbooks don't address AI risks. When you apply standard penetration testing methodologies without accounting for these AI-specific failure modes and attack surfaces, you leave vulnerabilities unexamined.

• Incomplete vulnerability coverage

Organizations frequently assess traditional security controls while neglecting AI-specific risks including prompt injection attacks, model manipulation through natural language exploitation, adversarial inputs, data poisoning, and jailbreak techniques. This incomplete assessment creates false confidence. Your executive leadership believes AI systems are secure because penetration tests passed, while adversaries exploit AI-specific vulnerabilities that traditional testing never evaluates.

• Expertise and integration gaps

You need expertise across multiple domains: traditional security, data science, machine learning operations, and domain-specific knowledge. Building red teams with the right mix of expertise and perspectives represents a fundamental challenge in a market with high demand for security professionals. Treating AI red teaming as periodic consultant engagements rather than continuous processes represents another common mistake. You need MLOps and CI/CD integration enabling routine testing with every model update.

AI Red Teaming Best Practices

Effective AI red teaming programs build on framework-based integration, balanced autonomous-human approaches, and continuous testing workflows.

• Framework-first implementation

Build on proven frameworks such as MITRE ATT&CK, complemented by NIST AI RMF for risk management structure, MITRE ATLAS for AI-specific threat taxonomy, and OWASP ML Top 10 for vulnerability classification.

• Hybrid autonomous-human strategy

Optimal enterprise security operations require strategic deployment of both autonomous and manual approaches. Autonomous approaches excel at systematic exploration of complex attack surfaces at scales impractical for human testers alone, while human expertise enables creative reasoning and contextual judgment about real-world exploitation likelihood.

• Continuous integration and lifecycle testing

AI red teaming integrates directly into development workflows for offensive automation, adversary emulation, and continuous testing. Industry consensus in 2024 highlights that success lies in combining autonomous testing tools with human expertise. Testing methodology should match the system's lifecycle stage, with different techniques appropriate for pre-deployment, development, and post-deployment phases.

How Organizations Benefit From AI Red Teaming?

Organizations that implement AI red teaming programs gain measurable advantages across security posture, regulatory standing, and operational resilience. These benefits compound over time as testing matures and findings inform broader security strategy.

Systematic AI red teaming delivers organizational value in several areas:

• Reduced incident response costs: Finding vulnerabilities before attackers exploit them eliminates the expenses associated with breach remediation, legal fees, and customer notification. Proactive testing costs a fraction of reactive incident response.
• Audit and compliance readiness: Documented red team assessments demonstrate due diligence to regulators, auditors, and insurance underwriters. Organizations can show evidence of systematic security validation when facing compliance reviews or cyber insurance renewals.
• Accelerated secure deployment: Development teams release AI systems faster when red team findings integrate into the build process. Early vulnerability identification prevents costly redesigns after production deployment.
• Informed security investment: Red team results quantify which defensive gaps pose the greatest risk. Security leaders allocate budgets based on demonstrated exposure rather than theoretical threat models.
• Cross-functional alignment: AI red teaming creates shared understanding between security, data science, and engineering teams. Joint exercises build relationships and establish common vocabulary for discussing AI risks.
• Third-party risk visibility: Organizations using vendor AI systems or APIs gain insight into risks they inherit. Red team assessments of third-party integrations reveal exposure that vendor documentation may not disclose.

These organizational benefits reinforce the technical advantages of vulnerability discovery and continuous validation. Security teams that communicate value in business terms build stronger executive support for sustained AI red teaming investment.

SentinelOne's Singularity Platform provides the validation capabilities, custom frameworks, and breach simulation integrations your red team operations require for continuous security testing.

• Detection validation through MITRE ATT&CK

The Singularity Platform found all 16 attacks and all 80 substeps in MITRE ATT&CK evaluations with no delays, providing baseline metrics for evaluating whether your security platform identifies complex, multi-step attack sequences your red team simulates.

• Custom detection framework with STAR

Storyline Active Response (STAR) converts hunt queries from Deep Visibility into autonomous detection logic that executes continuously across your environment. You turn queries into automated hunting rules that trigger alerts and responses, converting hunt queries into persistent detection logic.

• Threat correlation and attack investigation

Singularity's Storylines technology reconstructs complete attack chains across 80 ATT&CK technique steps in seconds, automatically correlating endpoint events into attack narratives. You validate whether simulated attacks are properly correlated and create scheduled threat hunting searches with STAR Rules. SentinelOne’s Offensive Security Engine™ with Verified Exploit Paths™ can also help predict attacks before they happen and stop emerging threats.

• AI-assisted security analysis with Purple AI

Red teaming generates massive amounts of data, thousands of simulated attack events, multiple attack chains, detection gaps across different scenarios. Analyzing these findings manually to understand what worked, what failed, and why consumes hours that your team could spend on remediation. This is where Purple AI transforms red teaming operations.

Purple AI enables security teams to explore red team findings through natural language queries rather than manual data hunting. 

Instead of requiring your analysts to construct complex queries or manually correlate events, your team can ask Purple directly by prompting questions or queries like:

• "Show me all prompt injection attempts that bypassed detection,"
• "Am I being targeted by FIN12?

Purple AI will present your results in real-world language. You can easily understand your risks with its intelligent summaries. You can also use its suggested follow up questions to conduct red teaming exercises and do further investigations.

Purple AI also correlates endpoint, cloud, and identity telemetry, providing enterprise-wide protection and response capabilities for endpoint and cloud workloads. Purple AI delivers up to 80% faster threat hunting and investigations, as reported by early adopters, through automatic correlation of attack chains. Purple AI supports your red team operations by providing AI-assisted analysis of detection gaps discovered during adversarial exercises. 

Continuous validation through breach simulation

SentinelOne's partnership with Keysight enables security teams to safely simulate threats and proactively validate security coverage. The SafeBreach integration allows SecOps teams to confidently validate that the Singularity™ Platformis deployed correctly through continuous breach and attack simulation.

The Singularity™ Platform validates your AI red team findings through MITRE ATT&CK-mapped coverage, while Purple AI accelerates investigation of discovered gaps from hours to minutes. Storylines technology correlates simulated attack sequences across your entire environment, and STAR enables you to convert red team discoveries into autonomous detection rules. We also recommend using Prompt Security by SentinelOne to protect against AI-powered LLM-based threats. It can prevent shadow AI usage, denial of wallet/service attacks, block unauthorized agentic AI actions, and ensure AI compliance. SentinelOne’s agentless CNAPP assists with AI Security Posture Management and can help you discover AI pipelines, models, and services for their effective management.

FAQs

What is an AI Red Team in cybersecurity?

An AI red team is a group of security professionals who simulate adversarial attacks against an organization's artificial intelligence systems. These specialists combine traditional penetration testing expertise with machine learning security knowledge to probe AI models for vulnerabilities. 

AI red teams test how models respond to malicious inputs, whether training data can be extracted, and if safety controls can be bypassed. Their findings help organizations secure AI deployments before attackers exploit weaknesses.

How does AI Red Teaming Relate to Cybersecurity?

AI red teaming extends traditional cybersecurity practices to address machine learning-specific risks. While conventional red teams test network defenses, application security, and physical access controls, AI red teams add testing for prompt injection, model manipulation, data poisoning, and jailbreak techniques. 

Both disciplines share the goal of finding vulnerabilities through adversarial simulation. AI red teaming integrates with existing security operations, using frameworks like MITRE ATT&CK alongside AI-specific taxonomies like MITRE ATLAS.

Does AI Red Teaming include LLM Safety Testing?

Yes. Large language model safety testing is a core component of AI red teaming programs. Red teams evaluate LLMs for harmful output generation, jailbreak susceptibility, prompt injection vulnerabilities, and training data leakage. 

Safety testing examines whether models can be manipulated to produce toxic content, bypass alignment controls, or reveal sensitive information. Organizations deploying customer-facing LLMs prioritize this testing to prevent reputational damage and protect users from harmful AI responses.

What is a Red Team?

A red team is a group of security professionals who simulate real-world attacks against an organization to test its defenses. Red teams adopt an adversarial mindset, using the same tactics, techniques, and procedures that actual attackers employ. 

The goal is to find vulnerabilities before malicious actors do and validate whether security controls work under realistic conditions. Red team exercises provide actionable findings that help security teams strengthen their defensive posture.

How does AI Red Teaming differ from traditional Penetration Testing?

AI red teaming addresses behavioral risks in how AI systems respond to adversarial inputs rather than just code-level vulnerabilities. Adversarial AI testing covers AI-specific attack vectors including prompt injection, model inversion, adversarial inputs, data poisoning, and jailbreak techniques that don't exist in traditional software. 

Effective AI red teaming extends beyond individual model vulnerabilities to address broader sociotechnical systems, including emergent behaviors from complex interactions between models, users, and environments.

What Frameworks should organizations use for AI Red Teaming?

Start with MITRE ATT&CK as your foundational framework for adversary emulation. Add NIST AI Risk Management Framework for risk structure, MITRE ATLAS for AI-specific threat taxonomy, and OWASP Machine Learning Top 10 for vulnerability classification. 

These complementary frameworks provide standardized measurement and enable cross-organizational collaboration.

Can autonomous AI red teaming replace human security testers?

No. Optimal strategies combine automation for systematic coverage with human expertise for creative attack scenarios and contextual judgment about real-world exploitation likelihood. 

You need both capabilities deployed strategically to their respective strengths. Automation excels at scale and speed, while human testers provide creativity and business context understanding.

How frequently should organizations conduct AI red teaming?

Integrate AI red teaming into MLOps and CI/CD workflows for continuous testing with every model update, retraining, or deployment. This continuous approach replaces periodic consultant engagements with persistent validation, enabling you to find security control drift as configurations change. 

Annual or quarterly assessments provide insufficient visibility into AI systems that evolve continuously.

What are the biggest implementation mistakes organizations make?

Organizations most commonly focus narrowly on model vulnerabilities while overlooking sociotechnical systems and emergent behaviors. They apply generic security approaches to AI-specific threats, test incomplete vulnerability dimensions, and treat red teaming as periodic engagements rather than continuous processes. 

Success requires comprehensive assessment spanning development practices, implementation architectures, and operational contexts.

How do you measure AI red teaming success?

Measure success through coverage metrics mapped to established frameworks like MITRE ATT&CK and MITRE ATLAS. Track the percentage of AI-specific attack vectors tested, mean time to find vulnerabilities, and false positive rates in your security controls. 

Document which adversary techniques your defenses stop versus those requiring remediation, and monitor security control drift between testing cycles.