Understanding the Security Implications of the Machine-Learning Supply Chain

26.03.2021.

Sven Herpig

The hopes and expectations connected to artificial intelligence are staggering. All major powers have started investing heavily in the research and development of artificial intelligence – especially machine learning. This progress may be driven by a goal that has been described in – an oversimplified but clear way – by Vladimir Putin. He has famously been quoted as saying that the nation that leads in artificial intelligence “will be the ruler of the world”. Countries such as the United States and China, and especially their respective private sectors, seem to have the upper hand in research and application right now. However, a vast number of affected sectors and possible specializations – such as securing artificial intelligence – enable a number of states and non-state-actors to meaningfully engage in this domain.

Unfortunately, drivers of technological developments frequently follow the “move fast and break things” mentality, sometimes resulting in destabilizing effects for the entire Internet ecosystem. Governments and companies must not repeat a grave mistake of the past: having security only as an afterthought. In order to create an enabling environment for the development and deployment of artificial intelligence, security considerations must urgently be addressed across the entire machine-learning supply chain.

Applications leveraging artificial intelligence will be highly integrated into the cyber domain and will likely experience adverse effects accordingly. These include but are not limited to geopolitical cyber operations, illegal transfer of intellectual property, national surveillance apparatuses, financial theft, and cybercrime. Every new technology attracts adversaries who will exploit it for their own gain, be it financially, politically, or otherwise motivated. Thus, there will be a number of capable and willing threat actors out there who want to meddle with systems powered by artificial intelligence.

Therefore, it is crucial to understand the supply chain and secure it against adversarial interference. The paper recommends decision-makers implement the following to achieve this goal:

  • Design a security approach rooted in conventional information security
  • Increase transparency, traceability, validation, and verification
  • Identify, adopt, and apply best practices
  • Require fail-safes and resiliency measures
  • Create a machine-learning security ecosystem
  • Set up a permanent platform for threat exchange
  • Develop a compliance-criteria catalog for service providers
  • Foster machine-learning literacy across the board

Source and complete article: stiftung-nv.de

ANBIETER/PARTNER
Experten- und Marktplattformen
Cloud Computing

Technologie-Basis zur Digitalisierung

mehr
Sicherheit und Datenschutz

Vertrauen zur Digitalisierung

mehr
Anwendungen

wichtige Schritte zur Digitalisierung

mehr
Digitale Transformation

Partner zur Digitalisierung

mehr
Energie

Grundlage zur Digitalisierung

mehr
Experten- und Marktplattformen
  • company
    Cloud Computing –

    Technologie-Basis zur Digitalisierung

    mehr
  • company
    Sicherheit und Datenschutz –

    Vertrauen zur Digitalisierung

    mehr
  • company
    Anwendungen –

    wichtige Schritte zur Digitalisierung

    mehr
  • company
    Digitale Transformation

    Partner zur Digitalisierung

    mehr
  • company
    Energie

    Grundlage zur Digitalisierung

    mehr
Values Blogs