Operator-grade
The team that scopes your work in Nelson is the team that runs it. The architects are the operators. Findings come from people who've actually exploited what they're describing — not desk research.
Independent ai governance & risk for Nelson-based tourism organisations — board-ready reporting mapped to NZ Privacy Act 2020 and NZISM. AI governance that engineering teams will actually use — model and dataset inventory, risk tiering, red-team requirements per tier, and approval workflows that do not become the AI bottleneck. Mapped to NIST AI RMF and ISO/IEC 42001 where it matters.
The tourism, seafood concentration around Nelson sees POS malware, loyalty-program account takeover and payment skimming. Our ai governance & risk work in Nelson is scoped against this real threat profile, not a generic checklist.
Every finding ships with a control reference against NZ Privacy Act 2020 and NZISM, with GCSB / NCSC NZ guidance cited where it changes the remediation priority. Board reporting follows the CERT NZ Critical Controls expectation set.
Basalt delivers ai governance & risk to organisations across Nelson and the wider Nelson region (population ~54k). The tourism, seafood sectors that anchor the region face a distinct threat profile — POS malware, loyalty-program account takeover and payment skimming — and our engagements are scoped to that, not a generic playbook. Reporting maps cleanly to the NZ Privacy Act 2020 and NZISM that New Zealand boards already use, with regulator context (GCSB / NCSC NZ) called out where it changes a remediation priority.
The team that scopes your work in Nelson is the team that runs it. The architects are the operators. Findings come from people who've actually exploited what they're describing — not desk research.
Local context matters: POS malware, loyalty-program account takeover and payment skimming. Basalt's Nelson engagements are scoped to the threat profile of tourism teams in Nelson, not a generic global checklist.
Where most regional providers are still testing for 2022 threat models, Basalt actively works agentic AI tool-abuse and indirect prompt injection at scale and identity-first attack chains across federated SaaS in production engagements. Forward-leaning, not theoretical.
Cyber security in New Zealand can't be done with last year's threat models. The Basalt practice runs against current attacker tradecraft — agentic AI abuse, MCP and AI-tool supply chain, post-quantum readiness — alongside the legacy infrastructure work that still keeps most organisations awake at night.
Most Nelson engagements scope inside one week and start within two. Retainer clients can trigger work the same day. We do not pipeline New Zealand clients through junior teams — a senior consultant scopes and runs the work end-to-end.
Both. Sensitive work — classified-adjacent environments, live incident response, OT walkthroughs — gets on-site time in Nelson and the wider Nelson region. Routine assessments and detection engineering run remote with a tight feedback loop.
Every finding ships with a control reference against the NZ Privacy Act 2020 and NZISM so your compliance team is not re-mapping our report. Where GCSB / NCSC NZ guidance exists for the specific finding, we cite it inline. Board-level reporting follows the CERT NZ Critical Controls expectation set.
The tourism sector concentration in Nelson drives a different threat model than a generic New Zealand engagement — POS malware, loyalty-program account takeover and payment skimming. Our scoping reflects that, and so does the test library we bring to the work.
Yes — this is core to how we work. Basalt actively researches and tests against agentic AI tool-abuse and indirect prompt injection at scale, MCP server and AI-tool supply chain compromise and identity-first attack chains across federated SaaS. Most regional providers haven't mapped these attack paths; we run them in production against client systems with explicit scope.
Strategic cyber security consulting
Adversarial testing for LLMs and AI systems
CREST-aligned penetration testing
Source code review and SAST/DAST integration