AI
Red Teaming
Expose GenAI vulnerabilities through continuous Automated AI Red Teaming and human-steered Vibe AI Red Teaming, resulting in explainable reporting and prioritized mitigation
Uncover real risks with attack simulations
AI systems can behave in unexpected ways, especially when pushed to the edge. With DeepKeep, you can automatically test your applications, agents and models under real-world conditions, or steer testing yourself using Vibe AI red teaming.
Get insights into where systems break down and how to fix them before it matters.
Simulate threats across your GenAI stack
Challenge your AI systems and evaluate how your custom AI applications, models, and agents respond to prompt injection, jailbreaks, data leakage attempts, and unsafe output generation. Tests run contextually and continuously, giving you a realistic view of how GenAI behaves under targeted misuse and not just ideal conditions.
The system adapts to your specific scenario, so you get relevant findings tied to your actual applications, not generic test cases.
For deeper, exploratory testing, Vibe AI red teaming lets you direct the process in natural language. Reddy, DeepKeep's AI red teaming agent, adapts attack paths in real time while you steer at each decision point.
Focus on what’s actionable
Every red teaming result is tied to a clear security or trust failure, with remediation guidance you can act on. Know which flows are affected, what triggered the failure, and what needs to change - whether that’s a policy update, prompt adjustment, or firewall guardrails. Findings are categorized by impact, so you can focus your effort where it matters most.
Here's how Automated and Vibe AI red teaming compare:
Automated AI Red Teaming
Vibe AI Red Teaming
Pre-programmed playbooks, scheduled runs (CI/CD), no user input during execution.
Natural language commands.
Limited. Includes some orchestration.
Real-time AI adaptation to defenses during execution (feedback loop).
Set schedules once; minimal intervention, focuses on monitoring reports.
Teams steer via conversation, with AI agent as an assistant.
Scheduled - CI/CD or a set cadence.
On-demand, human-led sessions.
Actionable reports on exposures. Kill-chain success rates.
Dynamic, audience-tailored insights with live steering.
Regression testing, coverage, compliance checklist.
Business-impact vulnerabilities. Deeper, exploratory testing.
Secure the future of your agents and apps
You don’t need to slow innovation to control risk.
With DeepKeep, you can enable AI across the business while maintaining visibility and control where it matters.
The business keeps building. You keep it secure.
FAQs
DeepKeep's AI red teaming combines two modes: automated testing that continuously simulates real-world attacks against your models, applications, and agents, and Vibe Red Teaming, a human-steered mode where your team directs testing in natural language while Reddy, DeepKeep's AI red teaming agent, executes and adapts in real time. Both are context-aware, evaluating how systems behave within actual usage scenarios to identify vulnerabilities before they can be exploited.
Vibe AI red teaming is DeepKeep's human-steered testing mode. Instead of predefined scripts, your team gives Reddy, DeepKeep's AI red teaming agent, an objective in plain language. Reddy generates attack paths and adapts as the system responds, pausing at key points so your team can review findings, redirect testing, or dig deeper into a potential vulnerability.
AI systems introduce dynamic and evolving risks that traditional security testing does not address. AI red teaming enables organizations to proactively identify weaknesses and continuously validate the security and safety of their AI systems.
It identifies a broad range of risks, including prompt injection, jailbreak susceptibility, data leakage, and unsafe outputs. It also evaluates trustworthiness issues such as hallucinations, bias, and inconsistent behavior that can impact reliability and compliance.
Manual testing is limited in scope and frequency. Automated red teaming enables continuous, large-scale testing across diverse attack scenarios, providing wider coverage and faster identification of vulnerabilities.
Automated AU red teaming runs continuously against predefined scenarios with no user input needed. It's ideal for broad, ongoing coverage and compliance. Vibe AI red teaming is interactive: your team directs testing in natural language and Reddy adapts in real time, which is better suited to chasing a specific, emerging, or business-critical risk. Most teams use both.
Yes. DeepKeep’s AI red teaming is model-agnostic and supports both LLMs and computer vision models, enabling consistent evaluation across multimodal AI systems.
Yes. It can be applied both before deployment and continuously in production to detect emerging risks as models, prompts, and usage patterns evolve.
It provides actionable insights and recommended mitigations, allowing teams to strengthen guardrails, refine policies, and improve model behavior based on real attack scenarios.
Findings from red teaming can be used to improve enforcement in the AI Firewall and inform usage policies in AI Lens, creating a continuous feedback loop between testing, visibility, and runtime protection.
AI red teaming helps organizations align with leading standards and frameworks such as GDPR, ISO 27001, OWASP Top 10 for LLMs and AI Agents, and MITRE ATLAS. By continuously identifying and validating risks, it supports audit readiness and strengthens overall AI governance.
Yes. It evaluates model, application and agent behavior across multiple languages, maintaining detection accuracy and ensuring vulnerabilities cannot be exploited through language-based variations.