AI / Agentic Development

Overview

AI and agentic systems introduce a fundamentally different risk profile to your organisation. Autonomous agents that can reason, access tools, and take actions on behalf of users require security thinking that goes well beyond traditional application security.

Opcode helps organisations design, build, and deploy AI-powered systems that are secure, controllable, and aligned with business objectives — from LLM-integrated applications to fully autonomous agent workflows.

What’s included

• Agentic system architecture — Designing multi-agent systems with appropriate guardrails, permission boundaries, and human-in-the-loop controls. Ensuring agents operate within defined scopes and fail safely.
• Secure AI implementation — Building LLM-powered applications with defence against prompt injection, data exfiltration, excessive agency, and model manipulation. Practical security controls, not theoretical checklists.
• AI risk assessment — Evaluating your existing or planned AI systems for security vulnerabilities, data privacy risks, and compliance gaps. Covering OWASP Top 10 for LLM Applications and emerging AI threat models.
• RAG and data pipeline security — Securing retrieval-augmented generation systems, vector databases, and data ingestion pipelines. Ensuring sensitive data isn’t inadvertently exposed through AI outputs.
• Tool-use and API security — Designing secure tool-calling patterns for agents that interact with external systems, databases, and APIs. Least-privilege access, input validation, and output sanitisation for autonomous operations.
• AI governance advisory — Helping leadership teams establish AI usage policies, acceptable use frameworks, and oversight mechanisms that balance innovation with risk management.

How it works

1. Discovery — Understand your AI objectives, existing systems, data landscape, and risk appetite.
2. Architecture — Design secure AI system architecture with appropriate controls, monitoring, and fallback mechanisms.
3. Implementation — Hands-on development and integration of AI systems, with security built in from the ground up.
4. Validation — Red-team testing of AI systems, adversarial prompt testing, and security review before production deployment.

Relevant frameworks

• OWASP Top 10 for LLM Applications
• NIST AI Risk Management Framework (AI RMF)
• ISO/IEC 42001 AI Management System
• Australia’s AI Ethics Framework
• MITRE ATLAS (Adversarial Threat Landscape for AI Systems)