Enterprise AI Security Assessment
Is your AI agent safe for production? DNA uses agentic red teaming (agents attacking agents) to assess whether AI agents, Copilots, AI Coding Assistants, or AI-enabled workflows can be trusted with real data, real tools, and real decisions.
Our Methodology
DNA approaches AI assessment from an offensive security perspective. A dedicated agentic workflow for AI Security: decomposing systems, mapping trust boundaries, modeling attack paths by business impact, and adversarial testing across the entire workflow, permissions, data access, tool controls, autonomy limits, and traceability. A specialist AI Security senior runs every engagement. The goal is a clear answer: is the AI system safe for production?
System Decomposition
Agents map architecture and data flow
The specialist AI Security senior defines trust boundaries
Threat Modeling
Automated attack surface analysis
The senior builds attack paths by business impact
Adversarial Testing
Adversarial agents generate test cases and execute abuse cases
The senior runs chain attacks and validates impact
Findings and Remediation
Automated severity scoring and evidence compilation
The senior writes business impact analysis and the remediation plan
System Decomposition
Agents map architecture and data flow
The specialist AI Security senior defines trust boundaries
Threat Modeling
Adversarial Testing
Findings and Remediation
Trust Boundaries and Permissions
Assess permission boundaries: what the AI can and cannot do, which data is off-limits, which actions require approval.
Data Access and Exposure
Verify whether the AI accesses more data than necessary, through RAG, knowledge bases, CRM, HRIS, or document stores.
Tool and Action Controls
Assess whether AI can be misdirected to abuse tools: email, APIs, CRM, file systems, shell access, refund workflows.
Approval and Escalation Controls
Test whether AI bypasses approval workflows, when it must stop for human confirmation, when it can act autonomously.
Memory and Context Persistence
Assess risks from long-term context: whether AI learns from bad data, retains invalid instructions, or carries unsafe behavior across sessions.
Logging and Investigation Readiness
Verify post-incident traceability: logs, replay, tool-call chains, correlation between inputs and AI actions.
When should you engage this service?
Before production rollout
An AI agent or system is about to be deployed into real workflows with real data and tools
Before granting action access
AI is being granted access to APIs, CRM, email, transactions, or decision-making on behalf of people
After major changes
Workflow changes, new connectors, permission updates, or expansion of AI scope and autonomy
When leadership needs assurance
CISO, CTO, or executive leadership needs a clear answer: is the AI safe for real deployment
Assessing AI systems demands real-world offensive security thinking. It is not enough to test model behavior. The assessment must cover the entire workflow: permissions, data access, approval controls, and the blast radius when AI acts on behalf of people. DNA brings nearly two decades of enterprise security experience to this challenge, plus first-hand agentic operations.
Certifications
Contact us about this service
Let DNA assess whether your AI agent is ready for real deployment, before granting access to data, tools, and decisions.