By revisiting security training through economics principles, organisations can navigate how to support effective security behaviour change

Here I describe analysis by myself and colleagues Albesë Demjaha and David Pym at UCL, which originally appeared at the STAST workshop in late 2019 (where it was awarded best paper). The work was the basis for a talk I gave at Cambridge Computer Laboratory earlier this week (I thank Alice Hutchings and the Security Group for hosting the talk, as it was also an opportunity to consider this work alongside themes raised in our recent eCrime 2019 paper).

Secure behaviour in organisations

Both research and practice have shown that security behaviours, encapsulated in policy and advised in organisations, may not be adopted by employees. Employees may not see how advice applies to them, find it difficult to follow, or regard the expectations as unrealistic. Employees may, as a consequence, create their own alternative behaviours as an effort to approximate secure working (rather than totally abandoning security). Organisational support can then be critical to whether secure practices persist. Economics principles can be applied to explain how complex systems such as these behave the way they do, and so here we focus on informing an overarching goal to:

Provide better support for ‘good enough’ security-related decisions, by individuals within an organization, that best approximate secure behaviours under constraints, such as limited time or knowledge.

Traditional economics assumes decision-makers are rational, and that they are equipped with the capabilities and resources to make the decision which will be most beneficial for them. However, people have reasons, motivations, and goals when deciding to do something — whether they do it well or badly, they do engage in thinking and reasoning when making a decision. We must capture how the decision-making process looks for the employee, as a bounded agent with limited resources and knowledge to make the best choice. This process is more realistically represented in behavioural economics. And yet, behaviour intervention programmes mix elements of both of these areas of economics. It is by considering these principles in tandem that we explore a more constructive approach to decision-support in organisations.

Contradictions in current practice

A bounded agent often settles for a satisfactory decision, by satisficing rather than optimising. For example, the agent can turn to ‘rules of thumb’ and make ad-hoc decisions, based on a quick evaluation of perceived probability, costs, gains, and losses. We can already imagine how these restrictions may play out in a busy workplace. This leads us toward identifying those points of engagement at which employees ought to be supported, in order to avoid poor choices.

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Consider unintended harms of cybersecurity controls, as they might harm the people you are trying to protect

Well-meaning cybersecurity risk owners will deploy countermeasures in an effort to manage the risks they see affecting their services or systems. What is not often considered is that those countermeasures may produce unintended, negative consequences themselves. These unintended consequences can potentially be harmful, adversely affecting user behaviour, user inclusion, or the infrastructure itself (including services of others).

Here, I describe a framework co-developed with several international researchers at a Dagstuhl seminar in mid-2019, resulting in an eCrime 2019 paper later in the year. We were drawn together by an interest in understanding unintended harms of cybersecurity countermeasures, and encouraging efforts to preemptively identify and avoid these harms. Our collaboration on this theme drew on our varied and multidisciplinary backgrounds and interests, including not only risk management and cybercrime, but also security usability, systems engineering, and security economics.

We saw it as necessary to focus on situations where there is often an urgency to counter threats, but where efforts to manage threats have the potential to introduce harms. As documented in the recently published seminar report, we explored specific situations in which potential harms may make resolving the overarching problems more difficult, and as such cannot be ignored – especially where potentially harmful countermeasures ought to be avoided. Example case studies of particular importance include tech-abuse by an intimate partner, online disinformation campaigns, combating CEO fraud and phishing emails in organisations, and online dating fraud.

Consider disinformation campaigns, for example. Efforts to counter disinformation on social media platforms can include fact-checking and automated detection algorithms behind the scenes. These can reduce the burden on users to address the problem. However, automation can also reduce users’ scepticism towards the information they see; fact-checking can be appropriated as a tool by any one group to challenge viewpoints of dissimilar groups.

We then see how unintended harms can shift the burden of managing cybersecurity to others in the ecosystem without them necessarily expecting it or being prepared for it. There can be vulnerable populations which are disadvantaged by the effects of a control more than others. An example may be legitimate users of social media who are removed – or have their content removed – from a platform, due to traits shared with malicious actors or behaviour, e.g., referring to some of the same topics, irrespective of sentiment – an example of ‘Misclassification’, in the list below. If a user, user group, or their online activity are removed from the system, the risk owner for that system may not notice that problems have been created for users in this way – they simply will not see them, as their actions have excluded them. Anticipating and avoiding unintended harms is then crucial before any such outcomes can occur.

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