Professor Peter Leonard

Professor Peter Leonard


B.Ec (Hons) LLM (Syd)

Business School
School of Management and Governance

Peter Leonard is a data and technology business consultant and lawyer.

Peter is a part-time Professor of Practice (across the Schools of Management and Governance, and Information Systems & Technology Management), within UNSW Sydney Business School.

He was a founding partner of Gilbert + Tobin Lawyers and led its technology and data practice over the firm’s growth from four partners to over eighty partners. Following his retirement as a G+T partner in 2017, he continues to assist G+T as a consultant. Peter has for over thirty years advised technology, data and communications businesses across Australia, New Zealand and Southeast Asia.

His business, Data Synergies, is a business and legal consultancy that assists businesses and other organisations to anticipate and address issues and concerns associated with deployment and use of advanced data analytics, including AI/ML and other automated decision making, and provision of services enabled by multiparty data ecosystems.

Peter serves on the NSW Government Artificial Intelligence Review Committee, tasked to review proposed applications AI, ML and automated decision making across NSW govermnat agencies.

Peter is also one of only two business sector members of the NSW statutory Information and Privacy Advisory Committee, tasked “to provide the government with information, advice, assistance and training to deliver world-leading information and privacy management practices”.

Peter also serves on a number of advisory boards, including for the National Farmers’ Federation’s Farm Data Project, the UNSW Canberra Institute for Cyber Security, and the Australian Digital Marketing Association’s Regulation Group.

Peter is immediate past chair of the Australian Computer Society’s Artificial Intelligence and Ethics Technical Committee, the IoT (Internet of Things) Alliance Australia’s Data work stream, and the Law Society of New South Wales’ Privacy and Data Committee.

  • Design and specification of privacy and data security by design data architectures, data handling practices and data analytics processes, clean rooms and technical and operational controls and safeguards, to derive value from data, data linkage and data analytics, while not exposing sensitive, trade secret or otherwise confidential data or enabling insights to be derived from data sets where possibility of this derivation will erode citizen or consumer trust.
  • Risk, governance, assurance and maturity frameworks, processes and tools for assessment and mitigation of legal, data security, regulatory and reputational risks associated with data handling and data analytics, and proper management of residual risks,
  • Evolving expectations of affected individuals and new frames of analysis as AI/ML harms to people and the environment, requirements for service interoperability, accessibility and inclusion, and developing societal and sectorial norms and expectations as to organisational responsibility and AI/ML ethics
  • Application to data and analytics business models of laws relating to trade secrets and confidential information, competition and antitrust, consumer protection, data privacy and information security, data localisation, communications carriage and content regulation, cybersecurity and encryption.