AI Security
Assess and secure AI applications, agents, RAG systems and LLM integrations.
AI systems introduce a new attack surface: prompt injection, retrieval poisoning, tool abuse, sensitive disclosure, and insecure integrations. SkyMind assesses these systems like attackers and secures them like engineers. We map the attack surface, run targeted tests against prompts, retrieval, agents, and integrations, and deliver concrete engineering fixes — guardrails, input filters, output sanitization, permission boundaries, and monitoring.
Security assessments and engineering controls for AI systems: threats, defenses, and ongoing monitoring.
What this engagement covers
Each capability is a scoped workstream with defined deliverables and acceptance criteria.
Attack surface mapping
Identify every input, tool, retrieval path, and integration that an attacker could influence.
Prompt injection & jailbreak testing
Test prompts, system messages, and retrieved content for injection and bypass paths.
Retrieval & data exposure testing
Test RAG systems for unauthorized retrieval, poisoning, and sensitive information disclosure.
Agent & tool abuse testing
Test agent permissions, tool scopes, and step limits for escalation and abuse paths.
What you walk away with
Tangible artifacts your team owns and operates — not a deck and a handshake.
Security assessment report
Findings ranked by risk with concrete engineering remediations.
Guardrail & filter implementation
Deployed input filters, output sanitization, and runtime guardrails.
Monitoring & alerting
Detection for abuse patterns, anomalies, and policy violations.
What changes after this engagement
Clear understanding of where your AI system is vulnerable
Concrete engineering fixes, not just findings
Ongoing detection for abuse and policy violations
Frequently combined with
Common questions about Security
Start AI Security
Tell us about your problem. We'll scope a path that fits your stack, timeline, and risk posture.