AI agent security engineering

We secure your AI Agents before they create chaos

CyberSE helps teams design, map, test, and harden AI agents, LLM stacks, model dependencies, and tool-connected workflows before they reach production.

Secure Your AI Agent Map Your AI Agent Start Red Teaming
How We Work

From messy AI adoption to controlled AI systems

We keep the engagement practical: identify the agent surface, map models and dependencies, test failure modes, then harden the system with clear controls.

01Discover agents, tools, prompts, models, and data flows
02Map LLM vendors, datasets, APIs, embeddings, and dependencies
03Test with Promptfoo, Garak, prompt injection, and tool-misuse cases
04Deliver fixes, guardrails, approval paths, and operating guidance
Sample Findings

Typical issues found during AI security reviews

Representative findings CyberSE reviews look for when assessing agentic systems.

Prompt injection bypass Critical Unsafe instruction handling in external content or retrieval flows.
Agent tool over-permissioning High Agents can invoke write actions without scoped approvals.
Sensitive RAG data exposure High Embeddings or retrieval contexts expose customer or internal data.
Missing approval workflow Medium Human review is absent for high-impact AI-triggered actions.
Cross-tenant memory leakage Critical Conversation or agent memory can cross customer boundaries.
Operating Intelligence

How CyberSE turns AI security signals into action

Signals are useful only when they become decisions. CyberSE frames AI security news, assessment results, and red-team findings around the controls teams can actually ship.

Source-grounded review

Threat notes stay tied to cited public sources, visible context, and analyst-style interpretation instead of unsupported claims.

Agent attack surface mapping

We map prompts, tools, APIs, models, memory, data flows, and approval paths so risks are connected to real AI architecture.

Practical control path

Recommendations focus on permissions, human gates, logging, prompt-injection tests, SBOM records, and remediation tickets.

Core Offerings

Security engineering for AI agents and LLM systems

View Services
Secure AI Agent Build visual Design and hardening

Secure AI Agent Build

Build agent workflows with scoped tools, approval gates, memory controls, logging, and prompt-injection resistant architecture.

For teams building production agents with tool access. Typical sprint: 8 weeks
  • Tool permission model
  • Human approval paths
  • Agent runtime controls
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AI Agent SBOM and LLM Mapping visual Inventory and exposure map

AI Agent SBOM and LLM Mapping

Document models, vendors, datasets, prompts, embeddings, plugins, APIs, and open-source dependencies so AI risk is visible.

For founders, CTOs, and compliance teams preparing AI controls. Typical sprint: 1 day
  • Model/vendor register
  • AI SBOM
  • LLM data-flow map
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AI Red Teaming visual Promptfoo, Garak, and adversarial tests

AI Red Teaming

Run structured tests for prompt injection, jailbreaks, data leakage, tool misuse, retrieval abuse, and unsafe outputs.

For AI products nearing launch, audit, or enterprise review. Typical sprint: 1 week
  • Promptfoo suites
  • Garak scans
  • Fix-prioritized report
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Useful Tools

Start with a focused next step

AI Security Readiness Assessment

Takes about 3 minutes. No signup required. Get prioritized AI risk recommendations and mapped next steps.

Run Assessment

AI Policy Generator

Draft practical AI use, vendor, data handling, and agent-control policy language.

Launch Generator

AI Security Companion

Ask the connected vCISO chatbot about prompt injection, SBOMs, LLM controls, and AI risk decisions.

Open Companion
Talk to AI CISO