Enterprises are shipping AI features into customer-facing products faster than most security teams can assess them. Chatbots handle account lookups. Generative agents draft customer emails. Retrieval-augmented systems pull answers from internal knowledge bases. Each of these creates a new attack surface that traditional application security testing does not cover. Prompt injection, training data leakage, jailbreaks, and abuse of connected tools require a different discipline. That discipline is AI red teaming.
What AI red teaming actually tests
AI red teaming evaluates AI systems the way a real adversary would. Group-IB’s team probes large language model deployments, retrieval-augmented generation pipelines, autonomous agents, and multi-modal systems for a set of failure modes that classic pen testing misses.
Prompt injection is the most publicized. Attackers embed instructions in documents, emails, or web pages that the model later processes, causing it to override its guardrails and take actions the developer never intended. Data injection expands this by poisoning the corpus a model retrieves from, so answers are biased or manipulated at scale. Model extraction attempts to steal proprietary weights or system prompts through carefully crafted queries. Jailbreaks defeat safety filters. Tool abuse exploits agents that can call external APIs, turning a helpful assistant into a mechanism for account takeover, data exfiltration, or financial transactions the user never authorized.
These are not theoretical risks. Group-IB investigators have documented real-world abuse of AI systems in fraud campaigns, brand impersonation operations, and phishing at scale. The Weaponized AI Whitepaper 2026 catalogs the tactics currently in circulation.
The brand, data, and digital risk surface AI exposes
AI red teaming intersects with brand, data, and digital risk in ways that most product teams do not anticipate.
On the brand side, an exploitable chatbot can be manipulated into producing content that damages the brand. Attackers post the transcripts on social media. Regulators take notice. Customer trust erodes. Group-IB’s digital risk protection team routinely finds instances of AI-generated brand abuse, including deepfake executive impersonations, cloned chat interfaces on look-alike domains, and fake support agents that harvest credentials by mimicking the brand’s AI assistant.
On the data side, retrieval-augmented systems can be coaxed into leaking sensitive information from their source corpus. This includes internal policies, customer records, and proprietary training data. AI red teaming reveals whether the guardrails hold under adversarial pressure, or whether a determined attacker can extract data the system was never supposed to expose.
On the digital risk side, agent-based systems that call external tools introduce a new class of abuse. An agent with access to email, calendars, or payment APIs can be redirected by prompt injection to send messages, book resources, or move funds. Red teaming exposes these paths before attackers find them in production.
How Group-IB approaches an AI red team engagement
Engagements start with scoping. The team maps the AI system’s inputs, outputs, tools, data sources, and integration points. From there, testing follows a structured methodology drawn from Group-IB’s offensive security practice, adapted for AI-specific failure modes.
Testers probe for direct prompt injection through user inputs and indirect injection through data the model consumes. They evaluate the resistance of safety filters to encoded, obfuscated, and multi-turn adversarial prompts. They check for training data exposure through membership inference and extraction attacks. They test tool-calling agents for unintended action chains. And they exercise multi-modal systems for image, audio, and document-based attacks that bypass text-only defenses.
The output is a prioritized report with reproducible proof-of-concept exploits, business impact framing for each finding, and specific remediation guidance. Developers get exactly what they need to fix the issue. Executives get a clear view of which risks materially threaten the business.
Where AI red teaming fits alongside other Group-IB services
AI red teaming does not replace traditional application security testing. It sits alongside it. Group-IB pairs AI red team engagements with penetration testing of the surrounding infrastructure, source code review of the model integration layer, and threat intelligence on the actors currently targeting AI systems. When the model itself is compromised, the response often needs Incident Response and Digital Forensics as well, since the blast radius can extend into the data stores and downstream systems the model touches.
Continuous monitoring after the engagement is where Digital Risk Protection continues the work. New brand impersonations, deepfake campaigns, and clones of the AI assistant surface after launch, and takedown infrastructure is what removes them at scale before customers are harmed.
What good AI security looks like in 2026
Organizations shipping AI responsibly do three things in combination. They red team the AI system before launch and on a continuous cadence as models and guardrails change. They monitor for external brand abuse and deepfake impersonation of their AI features. And they treat AI incidents with the same investigative discipline as any other breach, including forensics and attribution.
The threat landscape is moving quickly. Attackers are already using AI to accelerate fraud, phishing, and social engineering. Defenders need testing methodologies that anticipate this. AI red teaming, combined with digital risk visibility and incident response readiness, gives enterprises a defensible answer to the question every board is now asking about AI safety.
