Validate Whether Your Agentic AI Systems Actually Hold
AI red teaming, architecture review, and control validation for teams building with LLMs, coding agents, MCP, RAG, tool integrations, and multi-agent systems. Human-led testing, AI-assisted campaign workflows, and manually verified findings.
AI security services built around validation
Prove where your agentic AI system fails, understand why it fails, and validate whether the controls actually work.
AI Red Teaming & Control Validation
Adversarial testing against LLM and agentic applications with reproducible evidence, severity assessment, and practical remediation paths.
- Prompt injection and indirect prompt injection via documents, email, calendar, CRM, and RAG
- Tool misuse, MCP tool poisoning, privilege escalation, and delegation abuse
- Data exfiltration paths via rendering, tool calls, side channels, and agent workflows
- Guardrail bypass, system prompt extraction, and monitoring evasion
- Black-box, grey-box, and white-box testing with technical and executive reporting
Agentic AI Security Advisory
Architecture review, threat modeling, security requirements, and operating-concept support for enterprise agentic AI stacks.
- Coding-agent, MCP, RAG, and tool-use architecture review
- Threat modeling across model, implementation, infrastructure, and runtime
- Trust-boundary analysis: user/agent, agent/tools, agent/data, agent/agent
- Security control catalogue mapped to BSI, OWASP, NIST, and MITRE ATLAS
- MCP server and tool supply-chain risk assessment
Ongoing Security Partnership
Continuous advisory and validation for teams shipping AI features, coding-agent workflows, and agentic applications.
- Quarterly red team assessments as systems evolve
- New deployment and feature security validation
- Threat intelligence briefings on emerging AI attack vectors
- Executive reporting and AI security posture tracking
- Retest support and remediation validation
Enterprise Training & Enablement
Selected author-led training for teams that want to build internal capability around agentic AI red teaming and secure engineering.
- 3-day training on securing and red teaming agentic AI applications
- Hosted lab environment with a vulnerable agentic AI application
- Black-box → grey-box → white-box methodology transfer
- Monitoring, detection review, and sandboxing exercises
- Optional management summary and follow-up validation sprint
The Agentic AI Red Teaming Lifecycle
A structured operating model for moving from architecture and controls to adversarial evidence, remediation, and retesting.
Architecture & Scope
Understand the AI system, data flows, model access, tools, agent permissions, deployment boundaries, and business-critical workflows.
Threat Model & Trust Boundaries
Identify what can go wrong across user/agent, agent/tool, agent/data, agent/agent, and internal/external boundaries.
Control Mapping
Map concrete technical controls to BSI, OWASP, NIST, MITRE ATLAS, and enterprise-specific requirements.
Black/Grey/White-Box Red Teaming
Execute realistic attack scenarios with increasing information access, from external behavior to source-level analysis.
Detection & Monitoring Review
Review traces, logs, guardrail events, risk scores, and detection rules to understand what attacks are visible and what stays hidden.
Remediation & Retest
Translate findings into concrete fixes, support engineering teams, and validate whether implemented controls actually hold.
AI-assisted, human-verified red team operations
The methodology combines human-led adversarial testing with AI-assisted campaign generation, attack-surface enumeration, evidence collection, trace analysis, and report drafting. Findings are manually verified before delivery for exploitability, severity, business impact, and remediation quality.
From focused assessment to continuous validation
Engage for a targeted red team assessment, a security foundation review, a validation sprint, or selected team enablement.
Focused AI Red Team Assessment
A targeted adversarial assessment of one LLM or agentic AI system, focused on exploitability, evidence, and actionable remediation.
- Attack scenarios based on your actual stack
- Prompt injection, tool abuse, exfiltration, and guardrail bypass
- Technical report and executive summary
- Engineering debrief and remediation guidance
Agentic AI Security Foundation
Architecture review, threat modeling, control mapping, and operating-concept support for teams building agentic AI systems.
- Architecture and trust-boundary review
- Threat model and attack-surface map
- BSI, OWASP, NIST, and MITRE mapping
- Security requirement catalogue
AI Control Validation Sprint
Offensive validation of implemented controls against realistic attack chains and enterprise-specific AI workflows.
- Retest of implemented controls
- Detection and monitoring review
- Residual risk assessment
- Remediation debrief and next-step plan
Enterprise Training
Hands-on training based on a realistic vulnerable agentic AI lab and practical AI red teaming methodology.
- Black-box, grey-box, and white-box progression
- MCP, RAG, tool abuse, and multi-step agentic attack chains
- Monitoring, detection review, and sandboxing exercises
- Optional management outcome summary
Real systems, real AI security work
Practical experience across regulated environments, agentic AI advisory, product assessments, open source, standards, and enterprise enablement.
Financial-Sector AI Security
Practical AI security work in a regulated banking environment, including assessment of customer-facing AI systems, attack scenario development, control validation, and security enablement for engineering teams.
Agentic AI Security Advisory
Security advisory for enterprise agentic AI stacks involving coding agents, MCP-style tool integrations, containerized environments, model gateways, threat modeling, and BSI/OWASP-aligned control mapping.
AI Coding Platform Assessment
Security assessment of an AI-powered development platform with IDE integration, multi-tenant architecture, agent workflows, and code-assistance features.
Tool-Using Agent Assessment
Assessment of autonomous agent workflows with business-tool integrations, focusing on prompt injection, tool misuse, data exposure, permission boundaries, and unsafe delegation.
Microsoft PyRIT
Top contributor to Microsoft PyRIT, helping improve practical tooling for AI red teaming, adversarial testing, and campaign automation.
OWASP GenAI Security
Contributor to multiple OWASP GenAI Security Project guides, including red teaming, agentic threats, incident response, and securing agentic applications.
Enterprise Enablement
Author-led training format for technical teams working with LLM applications, coding agents, MCP, RAG, tool integrations, and agentic workflows.
AI Red Teaming in Practice
Plan, execute, and report AI red team engagements against LLMs and agentic systems.
A hands-on guide to finding and exploiting vulnerabilities in LLMs, agentic systems, and AI pipelines through structured labs and real attack techniques.
Topics include black-box, grey-box, and white-box AI assessments, threat modeling, reconnaissance, model fingerprinting, attack-surface mapping, prompt injection, data extraction, tool and agent exploitation, MCP server exploitation, supply-chain and deployment attacks, PyRIT integration, campaign automation, and reporting strategies for executives, engineers, and auditors.
Securing & Red Teaming Agentic AI Applications
Selected 3-day hands-on training for technical teams that want to understand, attack, monitor, and harden agentic AI applications in a realistic lab environment.
Capability transfer for technical enterprise teams.
Participants work against a realistic vulnerable agentic AI application with agent loop, RAG pipeline, MCP-style tools, tool use, guardrails, audit data, and selected monitoring and sandboxing exercises.
The training follows a clean Black-Box → Grey-Box → White-Box progression and uses L0–L3 security levels so teams can compare which controls block, detect, or fail against specific attack classes.
- For Data Scientists, ML Engineers, Developers, Security Engineers, Red Teamers, and technical leads
- Technically demanding but progressive: no specialized AI-security background required
- Hosted lab environment, no local setup required
- Uses methodology and lab concepts from AI Red Teaming in Practice, extended with private training scenarios
- First enterprise cohorts available from mid-July 2026
External behavior, prompt injection, recon
Participants start from portals, browser traffic, and error messages. They identify trust boundaries, perform active recon, fingerprint guardrails, and exploit direct and indirect prompt injection.
RAG, MCP-style tools, exfiltration, agent abuse
Participants use curated grey-box materials such as architecture notes, tool schemas, selected system-prompt extracts, threat-model excerpts, and prepared attack scenarios to analyze RAG, tools, and agent trust boundaries.
Monitoring, defensive controls, sandboxing
Repo access opens selected implementation details. Participants review audit data and traces, test L3 controls, discuss hardening options, and run an isolated coding-agent sandboxing exercise.
Volkan Kutal
Founder & Lead AI Red Team Engineer
- Author: AI Red Teaming in Practice — Packt, 2026
- AI Red Team Engineer @ Commerzbank AG
- OWASP GenAI Security Contributor
- Microsoft PyRIT Top Contributor
- Anthropic Invite-Only Jailbreak Program
- AIUC-1 Consortium Member
Practical AI security for enterprise systems
I help organizations building with LLMs and agentic AI understand how their systems fail under adversarial pressure — and how to turn that knowledge into concrete controls, monitoring, and secure engineering practice.
My work spans the full lifecycle: architecture review, threat modeling, AI red teaming, control validation, and team enablement.
As a contributor to OWASP GenAI Security guidance and Microsoft’s PyRIT framework, I combine offensive AI security research with practical experience in regulated enterprise environments.
My upcoming Packt book, AI Red Teaming in Practice, turns this methodology into a hands-on guide for planning, executing, and reporting AI red team engagements against LLMs and agentic systems.
Ready to validate your AI systems?
Request an AI red team assessment, control validation sprint, security advisory engagement, or selected team enablement.