AI Security Audits & Secure Deployment

Key Benefits

Elimination of data exfiltration risk through network isolation and private cloud migration

Hardened developer tooling with enforced sandboxing and immutable configuration

Centralized governance over all agent tool calls and external API interactions

Semantic defense layers that detect and block prompt injection and goal hijacking

Compliance-ready architectures supporting GDPR, SOC 2, and industry-specific regulations

Reduced operational risk from autonomous agents through least-privilege enforcement

Service Overview

As AI agents gain deeper access to codebases, data, and enterprise systems, the attack surface expands beyond traditional network security into semantic manipulation, prompt injection, and execution hijacking. Our AI Security & Secure Deployment service helps organisations design and implement defense-in-depth architectures for LLM-powered tools—from developer environments like Claude Code to autonomous agent frameworks—ensuring your proprietary data stays protected and your AI systems operate within strict governance boundaries.

Pain Points We Address

Proprietary data exposed through public API calls to LLM providers

No visibility into what AI agents are doing with tool access and code execution

Developers using AI coding tools without security guardrails

Prompt injection vulnerabilities in agents processing external data

Lack of governance frameworks for autonomous AI systems

Difficulty meeting compliance requirements for AI-assisted workflows

Our Approach

We begin with a threat assessment of your current AI tooling and agent deployments, mapping data flows, tool permissions, and network exposure. From there, we design a layered security architecture spanning identity, network, governance, and semantic defenses. We then implement and validate each layer—migrating to private cloud infrastructure, enforcing sandboxing, deploying centralised tool gateways, and configuring runtime guardrails. Finally, we establish monitoring and incident response procedures to maintain security posture as your AI usage evolves.

Example Use Cases

Migrating a trading firm's Claude Code deployment from public API to AWS Bedrock with PrivateLink and SSO integration.

Implementing an AgentCore Gateway to sanitise tool payloads and enforce on-behalf-of identity flows for a development team.

Auditing an autonomous agent framework for prompt injection vulnerabilities and deploying semantic guardrails.

Establishing governance policies for AI-assisted code review and deployment pipelines in regulated industries.

Configuring ephemeral, airgapped execution environments for agents processing sensitive financial or healthcare data.

Typical Deliverables

AI Security Threat Assessment Report

Defense-in-Depth Architecture Design

Hardened Configuration Playbook (Claude Code, agent frameworks)

AgentCore Gateway or MCP Proxy Implementation

Semantic Guardrail Configuration (Bedrock Guardrails, LLM-as-a-Judge)

Governance Policy Document and Compliance Mapping

Monitoring & Incident Response Runbook

What Makes Us Different

Hands-on expertise with Claude Code security hardening, including AWS Bedrock migration, sandbox enforcement, and AgentCore Gateway configuration.

Deep understanding of the agent threat landscape—prompt injection, data exfiltration, and execution hijacking—informed by real-world vulnerability research (e.g., CVE-2026-25725).

Framework-agnostic approach covering both managed cloud (AWS Bedrock) and self-hosted runtimes (OpenClaw), tailored to your risk profile.

Implementation of semantic defense layers including LLM-as-a-Judge evaluation, Bedrock Guardrails, and automated kill switches.

Why AI Security Requires a New Approach

Traditional cybersecurity focuses on network perimeters, access control, and deterministic software. AI agents break these assumptions fundamentally. Language models conflate data and instructions—an agent reading an external document can be manipulated by hidden instructions embedded within it. This creates attack surfaces that firewalls, WAFs, and conventional pen-testing simply cannot address.

The stakes are highest in environments where AI agents have access to proprietary code, trading algorithms, customer data, or production infrastructure. A single prompt injection exploit can turn a helpful coding assistant into a data exfiltration vector.

The Four Layers of AI Defense

We structure every engagement around four complementary security layers:

Identity & Access

Eliminate long-lived API keys in favour of short-lived, scoped credentials. We implement AWS IAM Identity Center (SSO) with OIDC federation, ensuring every agent session is tied to a specific human identity with auditable, time-limited permissions.

Network Isolation

Move AI workloads off public APIs and into your VPC. Using AWS PrivateLink and strict egress filtering, we ensure that model inference traffic never traverses the public internet—and that compromised agents cannot “phone home” to external servers.

Execution Governance

Deploy a centralised gateway (AgentCore or MCP Proxy) that intercepts every tool call an agent makes. This enables payload inspection, sensitive variable redaction, deterministic pattern matching, and on-behalf-of identity propagation—ensuring agents can only do what the initiating developer is authorised to do.

Semantic Defenses

Because agents are non-deterministic, we deploy independent monitoring layers: Bedrock Guardrails to block extraction of proprietary IP, LLM-as-a-Judge evaluators to assess whether agent actions align with security policies, and automated kill switches for anomalous behaviour.

Claude Code Hardening

For organisations using Claude Code as a development tool, we provide specific hardening measures:

Bedrock Migration: Shift from public Anthropic API to AWS Bedrock, keeping all code and conversation data within your VPC.
Sandbox Enforcement: Harden the bubblewrap-based sandbox with immutable configuration, restricted file access, and locked-down SessionStart hooks.
Configuration Management: Deploy centrally managed .claude/settings.json across all workstations, preventing ad-hoc permission escalation.
Credential Rotation: Automate credential retrieval via awsAuthRefresh with temporary session tokens—no static keys.

Agent Runtime Security

For autonomous agents built on frameworks like LangChain, LangGraph, or custom orchestration layers:

Ephemeral Environments: Run agent containers in airgapped, disposable environments with denied outbound internet access by default.
Least Privilege: Strip agents of all unnecessary capabilities—an analysis agent should physically lack write or delete credentials.
Semantic Filtering: Deploy independent filters that scrub sensitive variables from outgoing payloads and block known injection patterns in inbound data.
Human-in-the-Loop: Implement supervised agency for state-changing actions, requiring explicit human authorisation before an agent can execute trades, push code, or modify infrastructure.