AI
Security
Services
Helping you responsibly leverage AI to build applications and systems in a secure manner.
Risks introduced from the AI landscape
AI adoption is accelerating, and organisations are deploying systems that consume sensitive data and act on their behalf. This introduces a new and poorly understood attack surface: prompt injection, data leakage, excessive agency, and the compromise of connected tools and systems.
Adoption outpacing governance
Organisations have prioritised adoption of AI without establishing security guardrails or performing due diligence assessments to understand their security implications.
The cost of shadow AI
Employees are independently adopting AI tools without prior approval, creating an unmanaged and largely invisible layer of technology risk across the organisation.
Autonomous agent risk
AI agents, which take independent action on your systems and data, introduce a new category of risk which needs to be assessed and monitored.
Why a security framework is required
Modern deployments sit on a spectrum, from straightforward chatbots to multi-agent autonomous systems. Models are non-deterministic and as autonomy and access increase, a defence in depth approach is required.
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Wilbourne apply a structured framework, grounded in real-world deployment experience to understand your systems, identify their risks, and recommend layered controls proportionate to its autonomy and access.
AI Security Advisory and Threat Modelling
1
THREAT MODELLING
Understanding your agentic use cases, what access is required, and the types of data and interactions your agents would consume.
2
ARCHITECTURAL AND CONFIGURATION REVIEW
Review of your agentic deployments, with a focus on articulating and minimising the risks of the deployment. Applying defence in depth, so no single control failure leads to compromise.
Guard-railing and prompt engineering
Determining how agent actions are controlled and deviances from expected behaviour are minimised.
Sandboxing and containerisation
Ensuring agent runtimes are secured and restricted to limit the blast-radius of agent-compromise.
Privilege minimisation
Reviewing how tools, credentials, and supporting services are configured to ensure individual agents are appropriately restricted.
Logging and observability
Determining how malicious or unexpected agentic actions can be detected and tracked.
3
SECURITY ADVISORY
Providing recommendations on technical tooling, ways-of-working, and security controls to reduce the risk associated with your AI system. These will be based off the threat model and architecture review, with layered controls to reinforce on another rather than acting as single points of failure.
AI Security Penetration Testing
To review your existing security controls, a penetration test can be performed to identify AI-specific security weaknesses. This would include active attempts for prompt injection, breaking out of sandbox environments, and employing identity-based attacks.
Data Exposure Risk
Probe for
training data,
PII, credentials, or proprietary information
Prompt and Input Manipulation
Overriding system instructions via crafted user inputs, bypassing intended behaviour or safety constraints
Output Integrity Manipulation
Assessing where incorrect outputs could drive harmful downstream actions
Supply
Chain Integration
Assessing third-party plugins,
MCP servers, or connectors for vulnerabilities
Access Controls and Governance
Review whether appropriate access restrictions and governance processes are in place