AI & AI Agent Security
Secure AI agents before they can act on sensitive data, tools, and workflows.
Security review and design challenge for LLM applications, AI agents, RAG workflows, MCP and tool integrations, memory, data access, human approval, monitoring, and high-impact actions.
Discuss AI Agent SecurityAI agents create risk at the boundary between model behavior and real authority. Guvenkaya reviews the architecture, prompts, retrieval paths, tool permissions, memory, identity, approval gates, logs, and operating controls around AI systems so teams can deploy useful automation without granting unchecked access to sensitive data or business-critical actions.
What we review
LLM application and AI-agent architecture review
MCP, tool, plugin, and API permission review
Prompt injection and indirect prompt injection testing
RAG, document-ingestion, and untrusted-data boundary review
Memory, context, and cross-session isolation review
Agent identity, non-human identity, OAuth, and service-account review
Human approval and high-impact action control review
Monitoring, audit logging, cost-limit, and rollback review
Multi-agent orchestration and inter-agent trust-boundary review
AI supply-chain, model-provider, and prompt-template review
Best for
- • Customer-facing AI assistants that can access account, booking, payment, support, or personal data
- • Internal copilots or employee agents connected to enterprise search, SaaS, ticketing, HR, finance, or operational tools
- • MCP servers, tool-using agents, plugins, automations, or RPA workflows that can take action
- • RAG applications ingesting documents, emails, websites, tickets, knowledge bases, or third-party data
- • Production or near-production agents before launch, after a major prompt/tool/model change, or after expanding permissions
- • AI workflows where failure could affect customers, money movement, privileged access, compliance, operations, or trust
Scope themes
- • Direct and indirect prompt injection, goal hijacking, and instruction/data separation
- • Tool least privilege, per-tool authorization, sandboxing, and high-impact action validation
- • Agent identity, non-human identities, OAuth scopes, service accounts, and credential handling
- • Sensitive-data minimization across prompts, retrieved context, tool calls, outputs, and logs
- • Memory poisoning, persistent context integrity, retention, and user/session isolation
- • Human-in-the-loop controls, approval binding, step-up authentication, replay protection, and rollback paths
- • Monitoring for anomalous tool use, approval bypass attempts, data access, runaway loops, token cost, and drift
- • Multi-agent communication, trust boundaries, cascading failure prevention, and circuit breakers
- • Vendor, model-provider, prompt-template, agent framework, and dependency supply-chain risk
- • Adversarial test cases and release gates for prompt, tool, memory, retrieval, and provider changes
Typical outputs
- • AI-agent threat model and trust-boundary map
- • Tool and permission matrix with least-privilege recommendations
- • Prompt injection, tool-abuse, data-exfiltration, and memory-poisoning test results
- • High-impact action approval and execution-control findings
- • Monitoring, audit logging, rollback, and incident-readiness recommendations
- • Prioritized remediation roadmap and executive-ready risk summary
Start with this engagement
If this sounds close but not exact, start with the closest engagement. Guvenkaya can adjust scope during initial scoping.