AI is already in your organisation, approved or not. It brings new ways in for an attacker, new places for data to leak, and rules that arrived this year. We have secured AI systems inside regulated banks, and we can help you do the same.
AI adoption is moving faster than most security teams can follow. Large language models and agentic workflows bring their own attacks: prompt injection, training data poisoning, data exfiltration through API calls. These are happening now, across the major cloud platforms and in some cases at the AI providers themselves.
Shadow AI is normal now. Staff paste company data into tools nobody approved, and nobody in IT knows. That is sensitive data leaving the building and a compliance risk you cannot see. If you are APRA-regulated or run critical infrastructure, it is a regulatory problem as well as a security one.
We have done this work. Our engagements include agentic AI security design, LLM security review for financial services platforms, GenAI-based SIEM architecture, and AI governance for regulated industries.
The rules are arriving too. The National AI Centre's Guidance for AI Adoption sets six essential practices. Commonwealth agencies took on their first mandatory AI requirements on 15 June 2026, with full compliance due 10 December 2026, the same day the Privacy Act starts requiring disclosure of automated decision-making. Australian Standards for AI are expected in legislation in early 2027, and the Essentials series that replaces the Essential Eight may carry an agentic AI chapter. We built the practice to keep pace with this list, and we align your program to the parts that apply to you.
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.
A detailed 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. Our SHIELD-AI framework maps to APRA CPS 234 and CPS 230, SOCI, ISM/DISP, PSPF/DSPF and ISO/IEC 42001, so one set of controls answers several regulators. This includes AI vendor evaluation, data classification, human-in-the-loop review gates, and audit trail requirements.
Machine identities now outnumber people 109 to 1 in the average organisation, up from 82 to 1 a year earlier, and AI agents are the fastest growing share. Most breaches walk in through an identity. An agent holds credentials and acts on production systems, so we give each one its own identity, hard scope ceilings, delegation limits, and attribution back to the human who authorised it. This is the "own the harness" principle of our SHIELD-AI framework applied to access.
Where privileged access sits today, who and what holds it, and a staged path towards zero standing privilege. Covers admin accounts, break-glass, and the PAM tooling decision if you need one.
Discovery and lifecycle governance for service accounts, API keys, tokens and certificates: an owner for each, rotation that happens, and removal when the thing it served is gone.
Identity per agent, hard scope ceilings, delegation limits, a tiered autonomy model, and attribution of every autonomous action to the human who authorised it. We are building this with regulated clients now.
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 checks covered only a small sample, which left the bank carrying 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 now covers far more closed complaints than manual sampling did. The enterprise risk rating came down 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. We also designed a GenAI capability that analyses CVE data and generates 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 produce compliance assessments and quality findings 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.
A framework to take interaction QA coverage from 5% to near-complete monitoring, closing the compliance gap.
LLMs handle sensitive data, make business decisions and talk to your customers. They need the same discipline as any other critical system, and most organisations have not started. It is the same boring, disciplined security engineering it has always been, applied to a new kind of system.
AI adoption is not going to settle down, so waiting for it to is a plan to fall behind. We build AI security into your development practice, your governance and your architecture from the start. We have done it in banking.
We have designed, assessed and security-reviewed AI systems that run in regulated financial services, and we can walk you through them.
Governance that lets teams keep shipping: guardrails at the points that matter, with the 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.
APRA CPS 234, NIST AI RMF, ISO/IEC 42001 and OWASP LLM Top 10, applied on live engagements. We can show you where.
Wherever you are with AI, from evaluating a first tool to fielding agents across the business, we have done the security work in a regulated environment and can do it with you.