Services

AI safety is now a legal question, not just a technical one.

For most of the last decade, organisations treated artificial intelligence as an engineering problem. Build the model, test the outputs, ship the product. That era has ended.

AI systems now sit inside hiring decisions, credit assessments, clinical triage, insurance underwriting, public service delivery and legal process. When those systems fail, the consequences are not confined to a performance metric. They surface as discrimination claims, privacy breaches, regulatory enforcement, contractual disputes and reputational damage — and they land on boards, directors and general counsel, not on the data science team.

Super AI Safety exists at that intersection. We combine legal expertise with technical AI risk assessment to help organisations deploy artificial intelligence in ways that are defensible, documented and durable.

We work with organisations building AI, organisations buying it, and organisations that have discovered — often late — that they are already using it.

Our Services

1. Legal Advisory on Artificial Intelligence

AI has outpaced the contracts, policies and liability frameworks most organisations rely on. Standard vendor agreements rarely address model provenance, training data rights, output ownership, hallucination liability or the allocation of regulatory responsibility between provider and deployer. Existing professional indemnity cover may not respond to an AI-driven error. Confidentiality obligations may already have been breached by staff pasting client material into a public chatbot.

We advise on the legal exposure that AI creates, including:

  • AI vendor and procurement agreements — reviewing and negotiating licensing terms, service levels, indemnities, audit rights, data use restrictions and exit provisions
  • Liability allocation — determining who bears responsibility when an AI system produces a harmful, discriminatory or simply incorrect output, and structuring agreements to reflect that allocation clearly
  • Intellectual property — training data licensing, ownership of AI-generated outputs, infringement risk, and the protection of proprietary models and prompts
  • Privacy and data protection — assessing AI systems against the Privacy Act and Australian Privacy Principles, and against overseas regimes including the GDPR where cross-border data flows are involved
  • Confidentiality and professional obligations — particularly for firms in law, medicine, financial services and other regulated professions where AI use intersects with duties of care and client confidentiality
  • Regulatory interpretation — translating fast-moving and often ambiguous regulatory instruments into concrete operational guidance
  • Disclosure and consumer law — misleading and deceptive conduct risk arising from AI capability claims, automated decision-making disclosures, and consumer-facing transparency obligations

Our approach is practical. We do not deliver abstract risk taxonomies. We deliver advice that tells you what to change, in which document, by when.

2. AI Governance and Compliance Advisory

Regulators across every major jurisdiction are converging on a common expectation: organisations must be able to demonstrate, in writing, that they understand and control the AI systems they operate. Good intentions are not evidence. Governance is.

The regulatory environment is genuinely unsettled. Australia’s National AI Plan confirmed in December 2025 that the country would, for the time being, rely on existing laws and sector regulators supported by voluntary guidance and the Australian AI Safety Institute, rather than a standalone AI Act. In July 2026 the Prime Minister announced plans to legislate Australian Standards for AI and established an Office of AI within the Department of the Prime Minister and Cabinet. In Europe, the high-risk obligations under the EU AI Act carry an enforcement date of 2 August 2026, with a proposed deferral to December 2027 under the Digital Omnibus on AI that remains subject to formal adoption.

This uncertainty is not a reason to wait. It is a reason to build governance that holds regardless of which way the settlement falls.

We help organisations establish and operate AI governance frameworks, including:

  • AI governance frameworks and policy design — accountable ownership, decision rights, escalation pathways, approval gates and review cadence, scaled to the size and risk appetite of the organisation
  • AI system inventories and registers — identifying every AI system in use, including the shadow AI that most organisations do not know they have, and classifying each by risk
  • Regulatory mapping and gap analysis — assessing your obligations across applicable regimes, including the EU AI Act where you have European exposure, the Voluntary AI Safety Standard, sector-specific regulator guidance, and privacy and anti-discrimination law
  • ISO/IEC 42001 readiness — preparing AI management systems for certification against the international standard, and aligning with the NIST AI Risk Management Framework where relevant
  • Procurement standards — pre-deployment assessment criteria so that AI risk is evaluated before contracts are signed, not after
  • Incident response and escalation — defined processes for AI failures, near-misses and complaints, with the documentation trail regulators will ask for
  • Documentation and audit readiness — technical documentation, model cards, decision logs and record-keeping that will withstand scrutiny

The goal is a framework you can actually run, staffed by people you actually have.

3. AI Risk and Impact Assessments

Governance without assessment is paperwork. We conduct structured evaluations of AI systems to identify where they are likely to fail, who is likely to be harmed when they do, and what controls will meaningfully reduce that risk.

Our assessment work includes:

  • Algorithmic impact assessments — structured evaluation of an AI system’s effect on individuals and groups, including differential impact across protected attributes, and appropriate for both internal governance and regulatory submission
  • Pre-deployment safety evaluation — reviewing intended use, foreseeable misuse, boundary conditions, failure modes and the adequacy of human oversight before a system goes live
  • Bias and fairness testing — statistical assessment of model outputs across relevant cohorts, with clear articulation of the fairness definition applied and its trade-offs
  • Red-teaming and adversarial testing — deliberate attempts to induce harmful, unsafe, non-compliant or reputationally damaging behaviour, including prompt injection, jailbreaking and data extraction
  • Model and system risk reviews — assessment of training data provenance, evaluation methodology, monitoring, drift detection and the honesty of vendor capability claims
  • Human oversight design — evaluating whether the human in the loop can, in practice, exercise meaningful control, or whether they are functionally a rubber stamp
  • Third-party and supply chain assessment — evaluating AI capabilities embedded in vendor products, including the ones arriving through routine software updates

Each assessment produces a written report with findings graded by severity, specific remediation recommendations, and documentation suitable for board reporting and regulatory engagement.

4. Training and Board Education

The most common AI risk in an organisation is not a defective model. It is a capable, well-intentioned employee who does not understand what the tool is doing with their data, how confidently it will state something false, or when the decision in front of them should not be delegated to software.

We deliver education pitched at the actual level of the audience — no vendor pitch, no hype, no doom.

  • Board and executive briefings — director duties in the context of AI deployment, the questions boards should be asking management, oversight expectations, and how to read an AI risk report critically
  • General counsel and legal team training — AI-specific contract risk, regulatory obligations, privilege and confidentiality, and the responsible use of AI within legal practice itself
  • Staff training on responsible AI use — what these systems are, where they fail, what must never be entered into them, and how to verify output before relying on it
  • Technical team workshops — safety evaluation methodology, documentation standards, and building compliance requirements into development rather than bolting them on afterwards
  • Sector-specific programs — tailored to the regulatory environment and risk profile of your industry
  • Policy rollout support — helping AI policies land as understood practice rather than an unread document on the intranet

Sessions are delivered in person or remotely, as one-off briefings or structured programs.

How We Work

We start with what you actually have. Most engagements begin with discovery, because most organisations underestimate how much AI is already operating inside their business.

We are technology-literate and vendor-neutral. We do not resell AI products, take referral fees, or have a commercial interest in your choice of platform. Our only interest is that the deployment is sound.

We write things down. Every engagement produces documentation that can be handed to a board, a regulator, an insurer or a court.

We tell you when the answer is no. Some AI deployments should not proceed. We will say so, and explain why, rather than papering over a problem with a disclaimer.

We stay with it. AI systems drift, vendors change their terms, and the regulatory picture is moving quickly. We offer ongoing advisory arrangements for organisations that want their governance to remain current rather than becoming a snapshot of the day it was signed.

Who We Work With

  • Organisations deploying AI — corporates, professional services firms, financial institutions and healthcare providers who need to use AI safely and demonstrate that they are doing so
  • Organisations building AI — developers and startups who need regulatory readiness, safety evaluation and defensible documentation before they sell into regulated markets
  • Government and public sector — agencies requiring impact assessments, procurement guidance and policy frameworks that meet public accountability expectations
  • Boards and directors — who carry the oversight obligation and are entitled to independent advice about what management is telling them

Start the Conversation

An initial consultation costs you nothing but the time. We will discuss what you are deploying or building, identify the exposures that are most pressing, and tell you honestly whether you need our help — and if so, which parts.

Schedule a consultation

Excellence in AI Trust and Safety.

The information on this page is general in nature and does not constitute legal advice. The regulatory environment for artificial intelligence is changing rapidly and the position described here reflects our understanding as at the date of publication. Please seek advice specific to your circumstances.