Discover
We map your workflows, tools, data, and goals — where time is actually spent, which systems hold the work (email, CRM, spreadsheets, documents, support), and what constraints matter (data sensitivity, compliance, team skills).
How we work
Our AI Consulting Methodology
How Agent Palisade takes a small or mid-sized business from scattered AI experiments to integrated, productive, safe AI systems — and how our dedicated AI security review works when code is in scope.
Consulting engagements
From idea to production AI.
Every engagement follows the same arc, scoped to one workflow at a time so results show up in weeks, not quarters.
Prioritize
We rank AI opportunities by ROI, complexity, and risk, and agree on a sequenced roadmap. Most engagements start with one workflow where hours are measurably recoverable — not a platform rebuild.
Build & integrate
We build into the tools you already use rather than adding new ones: assistants grounded in your documents, automations connected to your CRM and inbox, and human review where it matters. Your team keeps working where they already work.
Secure & improve
We add governance, monitoring, review controls, and security checks so AI keeps delivering value safely — including periodic reviews as your usage and tools evolve.
AI security review
The AI Security Repo Audit methodology.
When an engagement includes our security review, this is how we run it — clear scope, prioritized findings, and practical remediation guidance.
Scope Definition
We identify the repo/application, languages, frameworks, AI providers, LLM usage, RAG components, agents, MCP tools, and infrastructure code in scope.
Automated Scanning
We run and complement proven, industry-standard tools — SAST/code patterns, secret, dependency, container, and IaC/cloud-config scanners — and add custom AI-specific checks. Tool output is a starting point we review and prioritize, not the whole picture.
AI-Specific Review
We review prompt construction, LLM API usage, RAG ingestion and retrieval, agent tool permissions, MCP tool definitions, output validation, model data flow, tenant isolation, and prompt/response logging.
Risk Prioritization
We prioritize findings based on severity, exploitability, data sensitivity, exposure, auth/data impact, production action risk, and whether an LLM or agent can trigger the behavior.
Remediation Guidance
Each finding includes what was found, why it matters, evidence, severity, recommended fix, and optional next steps.
Review Call
We review the report with your team and answer implementation questions.
Optional Ongoing PR Review
After the audit, teams can add PR-level scanning and recurring review.
Prioritization factors
- severity
- exploitability
- data sensitivity
- whether the issue is exposed to users
- whether it affects auth, data access, or production actions
- whether an LLM/agent can trigger the risky behavior
Methodology disclaimer
This review is designed to identify high-risk repository-level and AI-specific security issues. It does not guarantee that all vulnerabilities will be found and is not a substitute for a full penetration test, compliance audit, or formal legal/security certification.
AI Security Repo Audit
Ready to review your AI-enabled codebase?
Start with one repo. We will identify the highest-risk issues and give your team practical remediation steps.
Repo Audit
Security review for AI-assisted software development
- Repository, dependency, secret, container, and IaC review
- LLM, prompt, RAG, agent, and MCP risk review
- Prioritized report, risk score, remediation steps
- 60-minute review call