As AI becomes embedded in your organisation's operations, it introduces new attack surfaces, data risks, and governance challenges. We help you adopt AI confidently and securely, backed by real delivery experience in regulated environments.
AI adoption is accelerating faster than security teams can keep up. Large language models and agentic workflows introduce new attack vectors, from prompt injection and training data poisoning through to data exfiltration through API calls. These are not theoretical risks. They are happening today across the major cloud platforms and in some cases the AI providers themselves.
Shadow AI is already a reality in most organisations. Employees use unapproved AI tools with company data, often without IT knowledge or consent. Without visibility and governance, you are exposing sensitive data and creating compliance risks you do not even know about. For organisations operating under financial regulation or are deemed critical infrastructure, that exposure can carry serious regulatory consequences.
We have done this work in practice. Our engagements have spanned agentic AI security design, LLM security review for financial services platforms, GenAI-enhanced SIEM architecture, and AI governance for regulated industries. We know what good looks like because we have built it.
A structured review of your AI use cases, attack surface, and governance gaps. We map approved and shadow AI usage, assess risks against the OWASP LLM Top 10 and NIST AI RMF, and identify where controls need to be strengthened. Covers GenAI, agentic workflows, and classical ML.
Deep assessment of your LLM integrations and agentic workflows. We test prompt design, API configuration, data handling, access controls, and model hosting architecture. Abuse cases are developed for penetration testing, and findings are mapped to remediable controls your team can act on.
Design policies, oversight mechanisms, and controls for responsible AI adoption. We align your framework to relevant regulations including APRA CPS 234, PSPF/DSPF, and ISO/IEC 42001. This includes AI vendor evaluation, data classification, human-in-the-loop review gates, and audit trail requirements.
Map all AI use cases, models, integrations, and shadow AI across your organisation. Understand what is deployed, where data flows, and who has access.
Identify risks across the OWASP LLM Top 10 and NIST AI RMF. Develop abuse cases and test scenarios. Map findings to compliance obligations.
Build controls, governance policies, and secure AI architecture patterns. Define HITL review gates, access controls, data redaction, and audit trail requirements.
Establish ongoing visibility into AI risk posture. Define metrics and oversight processes that keep pace with evolving AI use cases and tools.
The following examples are from real engagements, with client names withheld.
A major Australian bank needed to automate quality assurance of complaint handling to meet ASIC RG 271 obligations. Manual processes covered only a small sample, creating an extreme-rated enterprise risk. We designed and security-assessed an agentic AI workflow that automates QA checks across retail and commercial complaints. Controls included data redaction before LLM ingestion, bias and fairness testing, human-in-the-loop review gates, and full APRA CPS 234 alignment. Abuse cases were developed to support penetration testing of the workflow.
Automated QA coverage of closed complaints significantly increased compared to manual sampling . Enterprise risk rating downgraded from Extreme.
Security analysts faced high volumes of log data requiring manual correlation, pattern recognition, and alert triage. We designed the security architecture for an LLM-based GCP Security AI Workbench integrated with the enterprise SIEM platform. The solution automates log parsing, SIEM rule generation and false positive suppression. Data classification and DLP controls were designed using Security AI Workbench prior to LLM ingestion. A parallel GenAI capability was architected to analyse CVE data and auto-generate enriched Jira tickets for vulnerability triage.
Reduced analyst effort for log triage. Improved detection coverage and faster mean-time-to-detect. Manual vulnerability ticketing replaced with automated, enriched Jira creation.
A banking platform was manually reviewing only 5% of customer interactions across chat and phone, leaving the vast majority unmonitored. We assessed a Vertex AI Gemini proof-of-concept that applies Natural Language Processing (NLP) to conversational data to generate compliance assessments and quality insights at scale. Security controls included customer data anonymisation, Vertex AI security configuration review, and development of abuse cases covering prompt injection and data exfiltration. Compliance validation was performed across the platform.
Framework in place to increase interaction QA coverage from 5% to near-complete monitoring, closing a significant compliance gap.
This is not a niche concern reserved for AI teams. LLMs process sensitive data, make business decisions, and interact with your customers. They need to be secured with the same rigour as any other critical system and most organisations haven't started yet.
Security teams that wait for AI adoption to stabilise will find themselves permanently behind. We help you build AI security in from the beginning, as part of your development practices, your governance framework, and your enterprise architecture. We have done it in banking. We can do it for you.
We have designed, assessed, and security-reviewed real AI implementations in regulated financial services environments, not simply quoted frameworks in powerpoint decks.
We design AI governance that enables innovation rather than blocking it. The right guardrails, applied at the right points, with regulatory obligations built in from the start.
We translate between security, data science, and business teams. Governance only works when everyone understands it, and we make sure they do.
We bring working knowledge of APRA CPS 234, NIST AI RMF, ISO/IEC 42001, and OWASP LLM Top 10, applied to live engagements, not just cited in documents.
Whether you are evaluating AI tools, building LLM integrations, or scaling AI across your organisation, we can help you do it securely and responsibly. We have done this work in regulated environments and we know what it takes to get it right.