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Liaison statement
Liaison statement from ETSI ISG SAI on Securing Artificial Intelligence

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State Posted
Submitted Date 2019-12-09
From Contact Sonia Compan
To Group IETF
To Contacts The IETF Chair <>
Cc The IESG <>
The IETF Chair <>
Response Contact
Purpose For information
Attachments SAI(19)001010r1_Liaison_statement_from_ETSI_ISG_SAI
This is to announce that the Kick-off Meeting for the new ETSI ISG on Securing
Artificial Intelligence (ISG SAI) was held on 23 October 2019.

The intent of the ISG SAI is to address 3 aspects of AI in the standards domain:

1. Securing AI from attack e.g. where AI is a component in the system that
needs defending.

2. Mitigating against AI e.g. where AI is the ‘problem’ (or used to improve and
enhance other more conventional attack vectors).

3. Using AI to enhance security measures against attack from other things e.g.
AI is part of the ‘solution’ (or used to improve and enhance more conventional

The ETSI ISG SAI aims to develop the technical knowledge that acts as a
baseline in ensuring that artificial intelligence is secure. Stakeholders
impacted by the activity of this group include end users, manufacturers,
operators and governments.

At the first meeting the following New Work Items were agreed:

AI Threat Ontology
The purpose of this work item is to define what would be considered an AI
threat and how it might differ from threats to traditional systems. The
starting point that offers the rationale for this work is that currently, there
is no common understanding of what constitutes an attack on AI and how it might
be created, hosted and propagated. The AI Threat Ontology deliverable will seek
to align terminology across the different stakeholders and multiple industries.
This document will define what is meant by these terms in the context of cyber
and physical security and with an accompanying narrative that should be readily
accessible by both experts and less informed audiences across the multiple
industries. Note that this threat ontology will address AI as system, an
adversarial attacker, and as a system defender

Data Supply Chain Report
Data is a critical component in the development of AI systems. This includes
raw data as well as information and feedback from other systems and humans in
the loop, all of which can be used to change the function of the system by
training and retraining the AI. However, access to suitable data is often
limited causing a need to resort to less suitable sources of data. Compromising
the integrity of training data has been demonstrated to be a viable attack
vector against an AI system. This means that securing the supply chain of the
data is an important step in securing the AI. This report will summarise the
methods currently used to source data for training AI along with the
regulations, standards and protocols that can control the handling and sharing
of that data. It will then provide gap analysis on this information to scope
possible requirements for standards for ensuring traceability and integrity in
the data, associated attributes, information and feedback, as well as the
confidentiality of these.

Security Testing of AI
The purpose of this work item it to identify objectives, methods and techniques
that are appropriate for security testing of AI-based components. The overall
goal is to have guidelines for security testing of AI and AI-based components
taking into account of the different algorithms of symbolic and subsymbolic AI
and addressing relevant threats from the work item “AI threat ontology”.
Security testing of AI has some commonalities with security testing of
traditional systems but provides new challenges and requires different
approaches, due to (a) significant differences between symbolic and subsymbolic
AI and traditional systems that have strong implications on their security and
on how to test their security properties, (b) non-determinism since AI-based
systems may evolve over time (self-learning systems) and security properties
may degrade, (c) test oracle problem, assigning a test verdict is different and
more difficult for AI-based systems since not all expected results are known a
priori, and (d) data-driven algorithms: in contrast to traditional systems,
(training) data forms the behaviour of subsymbolic AI. The scope of this work
item is to cover the following topics (but not limited to): • security testing
approaches for AI • testing data for AI from a security point of view •
security test oracles for AI • definition of test adequacy criteria for
security testing of AI • test goals for security attributes of AI and provide
guidelines for security testing of AI taking into account the abovementioned
topics. The guidelines will use the results of the work item "AI Threat
Ontology" to cover relevant threats for AI through security testing, and will
also address challenges and limitations when testing AI-based system. The work
items starts with a state-of-the-art and gap analysis to identify what is
currently possible in the area of security testing of AI and what are the
limitations. The works will be coordinated with TC MTS.

The ISG is also discussing adoption of a work item on:
Securing AI Problem Statement
This work will define and prioritise potential AI threats along with
recommended actions.

ETSI ISG SAI believes that this work will be of interest to many other
technical standards groups and looks forward to engaging with such groups.