Information Safety

Improving technology through lessons from safety.

SIRAcon 2024

SIRAcon 2024 wrapped up last Thursday, and once again the conference didn’t disappoint! We had an amazing line-up of speakers this year, and all of the talks were excellent! We also had a diversity of talks; academic and non-academic, quantitative and qualitative approaches, confidentiality and availability, technical and human risk. All but one of the talks were recorded and currently available to attendees on the conference website and will be available to members on the SIRA website after they are uploaded.

Once again, I was honored to be selected as a speaker, and my talk, “UnFAIR: Simplifying and Expanding Technology Risk Quantification,” was well received, and even generated some agreeable disagreement! While some disputed my assertion that FAIR (Factor Analysis of Information Risk) is not suited to risks outside of cybersecurity, I stand by my assertion: while FAIR can be used to quantify any risk, the language, once you get past the top level, is specific to the cybersecurity domain, and can be confusing for other types of risk, such as operational risk.

quantrr

In my talk, I presented a demo of a new Risk Quantification tool designed to be more flexible, use statistical distributions that better fit the data, and ultimately meet the needs of an analyst who is trying to start a risk quantification practice with limited time and no budget.

Since attendees expressed interest in trying out the tool, I wanted to make it more accessible, including to people with little or no experience using R. To address this, I spent time this week repackaging the demo and am excited to announce version 1.0 of quantrr (QUANT-R)! quantrr is bundled as an R package (library) and includes documentation on how to quickly get started using a “standalone” version and how to integrate quantrr into your existing R analysis and publishing workflow. Using the Quick Start instructions, you should be able to have an example report based on the demo up and running in less than an hour.

If you’ve learned FAIR or read Hubbard’s How to Measure Anything and want to get started quantifying risk with something other than Excel, give quantrr a try!

It’s very much an MVP release, by design, to get early reactions. I already have some things that I’d like to improve, and I would greatly appreciate feedback from any and all who give it a try, either sent to me directly or through GitHub. Reproducible bugs, feature requests, and pull requests are all welcome, I’d love to see it evolve into a community supported project and submitted to CRAN to become an official R package!

Slides

Slides from my talk are available here, and this is the link from the QR code I shared at the end.

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Does Phishing Training Work?

I was recently talking with a couple of friends who both work in technology outside of cybersecurity, and our conversation led to one of the most common interactions with the security team: phishing training. Their general experience reflected my own: companies generate simulated phishing emails, send them out periodically, and deliver training to employees that click.

This raises an important question, Does phishing training work? In the spirit of the Safety of Work podcast, let’s examine the evidence.

Academic Literature

What does the academic literature have to say? A quick search in Google Scholar found a literature review paper on the topic from 2020, “Don’t click: towards an effective anti-phishing training. A comparative literature review.” The paper reviewed 104 papers from 2003-2020 that researched questions on phishing attack success rates and/or training effectiveness.

“There is a large body of publications that confirm a decreased likelihood that users will fall victim to phishing messages after educating them with general anti-phishing material or via embedded training.”

There was a consensus that embedded training (simulated phishing emails with education for people who click the link) improved outcomes through reduced click rates, although there was no consensus on how best to educate users. Training needs to be repeated periodically to remain effective, at least every 5 months, preferably quarterly.

The paper also recommends providing a mechanism to report suspicious emails, like the increasingly common “report phishing” button, and also to tailor the level of difficulty of training to the individual, something that is not done in current commercial systems.

Data

What does the data say? Outside the academic literature, a series of reports by Cyentia and Elevate Security offer additional insights.

The first report, published in 2021, had a number of interesting findings:

  • Completing training 1-3 times reduces average click rates, but performance gets progressively worse for 4 and 5 times; average training rates for 5 training sessions was higher than none at all!
  • Sending more simulation emails decrease average click rates, even at high numbers of simulations, but flattens out just below 5%

Similar results are reported in the literature review paper: security fatigue is a real problem, and one study cited found two groups that were not affected by training - the “always click (11%)” and “never click (22%)”. In a presentation at Secure360 2015, the CEO of Cofense (then PhishMe) also noted that some users (between 5-10%) would always click on the link, no matter how much training they’d received.

Importantly, the Cyentia/Elevate report also noted that 100% of organizations eventually click or are compromised - that is, no matter how much you train, someone within your organization will click the phishing link.

A second report studied the problem in greater detail, finding that:

  • Some users get many more phishing emails than others (100s per year vs. a few).
  • The more emails a department gets the better they are at blocking them.
  • Most users won’t click the emails that do make it to their inboxes.
  • But some of those who do will click a lot (as much as one click per week).
  • Subjecting all users to the same level/type of treatment is counterproductive.

What the analysis showed was that nearly 80% of users never click a phishing link, and 4% account for 80% of clicks - a small number of high-risk users are the biggest source of phishing clicks.

(A third report studied the question of high-risk users in greater detail.)

My Experience

My own experience, reflected in my recent discussion, is that nearly everyone uses embedded training and has a “report phishing” button or a reporting email address, but does little beyond that. At worst, it is a game played by cybersecurity team to see how many people they can fool, using email templates with substantial inside knowledge (and not a realistic example of what they are likely to receive). At best, it uses realistic examples, periodic training, and provides feedback, with the goal of 0% click rate. Feedback when reporting phishing is uncommon, for either simulated or real phishing emails.

Education and training typically focuses on identifiers of phishing emails, like unusual links, email addresses, a sense of urgency, and spelling/grammatical errors. (Interestingly, the literature review paper suggests that looking for spelling errors is not an effective approach)

Training Differently

So, does phishing training work? Yes, with significant limitations. It’s clear that the embedded training and reporting button used by most companies helps improve average employee performance, but there is a small group (4-5%) of users that will always fall victim to phishing.

How can we improve? To answer that, I suggest we look at the problem through the lens of Security Differently, and shift the focus from preventing negative outcomes (clicking the link) to promoting positive capacities (reporting and blocking phishing).

If we can never get to a 0% click rate, what should be the goal of phishing training? We want to encourage employees to report suspicious emails (a measurable, positive action) so that we can take action and proactively block those emails and links. This is an important difference, illustrated well by this article, which shares the story of “Vicky”, who correctly identifies a phishing email but doesn’t report it, because “it’s not my thing to deal with.”

Focusing only on awareness, as most phishing training does, is only one part of what leads to the desired behavior (reporting) - the Capability. We also need to provide the Opportunity by making it as easy as possible to report by adding a reporting button, and Motivation through feedback - thanking people for reporting, and by following up if the email is not reported, even if the person didn’t click the link.

Even with high rates of reporting, we still have an organizational challenge: someone, somewhere, will click the link. To solve this, we can leverage the fact that most phishing emails will be sent to multiple people within our organization (or elsewhere). If we automatically block emails and links based on early reporting, we can stop the first click before it happens, and effective.

Ironically, this approach was the core content of the Cofense presentation at Secure360 in 2015: they created a system where employees were assigned a “credit rating” based on the reliability of their reports, as judged by security analysts. The system was designed to automatically block copies of emails that passed a threshold of reliability and number of reports, without human intervention of the security team.

Unfortunately, I’ve never come across an organization that fully adopted this model. If your organization has, please get in touch, I’d love to hear from you about the real-world effectiveness of this approach!

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SIRAcon 2023

As I’ve been posting, I’m cataloging and posting my past presentations, and this is the last one! This talk from SIRAcon 2023 summarizes my experiences leading Site Reliability Engineering (SRE), “Measuring and Communicating Availability Risk”.

The particular focus of my SRE work was on measuring availability and availability risk, and I learned quite a bit over the 3 years or so I did SRE. One of the key lessons was that the value of measuring availability using Service Level Objectives (SLOs) was for decision support (SIRA helped with this framing). That is, SLOs and the associated measurements help make decisions about what to do, either during an incident, tactically over the course of a month, and strategically over the course of several months and into the future.

Our biggest success was the result of measuring availability in ways that supported all three timescales, using a explicitly defined customer-focused measure of “available”, we were able to construct visualizations that helped during incidents (real-time), during maintenance planning (one month), and for longer-term work (many months).

A key element of this success was the business imperative: the work supported a large and important client, who had just negotiated a significant increase in availability by no longer allowing us to count scheduled downtime against our availability target. The Service Level Indictor (SLI) we created helped our incident responders understand outages better, and the SLO we created allowed our teams to schedule maintenance with confidence or confidently defer it. A hidden benefit was that the metrics, being based on direct observations from our monitoring tools, brought together and aligned the different stakeholders on a common view of how available our systems were - the new approach we developed was even adopted by our client as an improvement.

A copy of my slides are here, and the visual notes from the talk are below! As a bonus, you’ll get to see a photo of my dog, Gertie, which was added at the last minute as part of an ongoing cats vs dogs competition at the conference.

visual notes

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