How Not to Design an Error Message

SC07FireAlarm

The voice shouts out: “Detector error, please see manual.” Just once, then a few hours later. And when I did see the manual, I discovered that it means “Alarm has reached its End of Life

No, really. That’s how my fire alarm told me that it’s at its end of life. By telling me to read the manual. Why it doesn’t say “device has reached end of life?” That would be direct and to the point. But no. When you press the button, it says “please see manual.” Now, this was a 2009 device, so maybe, just maybe, there was a COGS issue in how much storage was needed.

But sheesh. Warning messages should be actionable, explanatory and tested. At least it was loud and annoying.

2017 and Tidal Forces

There are two great blog posts at Securosis to kick off the new year:

Both are deep and important and worth pondering. I want to riff on something that Rich said:

On the security professional side I have trained hundreds of practitioners on cloud security, while working with dozens of organizations to secure cloud deployments. It can take years to fully update skills, and even longer to re-engineer enterprise operations, even without battling internal friction from large chunks of the workforce…

It’s worse than that. Yesterday Recently on Emergent Chaos, I talked about Red Queen Races, where you have to work harder and harder just to keep up.

In the pre-cloud world, you could fully update your skills. You could be an expert on Active Directory 2003, or Checkpoint’s Firewall-1. You could generate friction over moving to AD2012. You no longer have that luxury. Just this morning, Amazon launched a new rev of something. Google is pushing a new rev of its G-Suite to 5% of customers. Your skillset with the prior release is now out of date. (I have no idea if either really did this, but they could have.) Your skillset can no longer be a locked-in set of skills and knowledge. You need the meta-skills of modeling and learning. You need to understand what your model of AWS is, and you need to allocate time and energy to consciously learning about it.

That’s not just a change for individuals. It’s a change for how organizations plan for training, and it’s a change for how we should design training, as people will need lots more “what’s new in AWS in Q1 2017” training to augment “intro to AWS.”

Tidal forces, indeed.

The Dope Cycle and the Two Minutes Hate

[Updated with extra links at the bottom.]

There’s a cycle that happens as you engage on the internet. You post something, and wait, hoping, for the likes, the favorites, the shares, the kind comments to come in. You hit reload incessantly even though the site doesn’t need it, hoping to get that hit that jolt even a little sooner. That dopamine release.

A Vicious cycle of pain, cravings, more drugs, and guilt

Site designers refer to this by benign names, like engagement or gamification and it doesn’t just happen on “social media” sites like Twitter or Instagram. It is fundamental to the structure of LinkedIn, of Medium, StackExchange, of Flickr. We are told how popular are the things we observe, and we are told to want that popularity. Excuse me, I mean that influence. That reach. And that brings me to the point of today’s post: seven tips to increase your social media impactfulness. Just kidding.

Not kidding: even when you know you’re being manipulated into wanting it, you want it. And you are being manipulated, make no mistake. Site designers are working to make your use of their site as pleasurable as possible, as emotionally engaging as possible. They’re caught up in a Red Queen Race, where they must engage faster and faster just to stay in place. And when you’re in such a race, it helps to steal as much as you can from millions of years of evolution. [Edit: I should add that this is not a moral judgement on the companies or the people, but rather an observation on what they must do to survive.] That’s dopamine, that’s adrenaline, that’s every hormone that’s been covered in Popular Psychology. It’s a dope cycle, and you can read that in every sense of the word dope.

This wanting is not innocent or harmless. Outrage, generating a stronger response,
wins. Sexy, generating a stronger response, wins. Cuteness, in the forms of awwws, wins. We are awash in messages crafted to generate strong emotion. More, we are awash in messages crafter to generate stronger emotion than the preceding or following message. This is not new. What is new is that the analytic tools available to its creators are so strong that the Red Queen Race is accelerating (by the way, that’s bait for outraged readers to insist I misunderstand the Red Queen Race, generating views for this post). The tools of 20th century outrage are crude and ineffective. Today’s outrage cycle over the House cancelling its cancellation of its ethics office is over, replaced by outrage over … well, it’s not year clear what will replace it, but expect it to be replaced.

When Orwell wrote of the Two Minutes Hate, he wrote:

The horrible thing about the Two Minutes Hate was not that one was obliged to act a part, but that it was impossible to avoid joining in. Within thirty seconds any pretense was always unnecessary. A hideous ecstasy of fear and vindictiveness, a desire to kill, to torture, to smash faces in with a sledge hammer, seemed to flow through the whole group of people like an electric current, turning one even against one’s will into a grimacing, screaming lunatic. And yet the rage that one felt was an abstract, undirected emotion which could be switched from one object to another like the flame of a blowlamp.

I am reminded of Hoder’s article, “The Web We Have to Save” (4.4K hearts, 165 balloons, and no easy way to see on Medium how many sites link to it). Also of related interest is Good-bye to All That Twitter and “Seattle author Lindy West leaves Twitter, calls it unusable for ‘anyone but trolls, robots and dictators’” but I don’t think Twitter, per se, is the problem. Twitter has a number of aspects which make trolling (especially around gender and race issues, but not limited to them) especially emotionally challenging. Those are likely closely tied to the anticipation of positivity in “mentions”, fulfilled by hate. But the issues are made worse by site design that successfully increases engagement.

I don’t know what to do with this observation. I have tried to reduce use of sites that use the structures of engagement: removing them from my reading in the morning, taking their apps off my phone. But I find myself typing their URLs when I’m task switching. I am reluctant to orient around addiction, as it drags with it a great deal of baggage around free will and ineffective regulation.

But removing myself from Twitter doesn’t really address the problem of the two minutes hate, nor of the red queen race of dope cycles. I’d love to hear your thoughts on what to do about them.


[Update: Related, “Hacking the Attention Economy,” by danah boyd.]

[Update (8 Feb): Hunter Walk writes “Why Many Companies Mistakingly Think Trolls & Harassment Are Good for Business,” and I’d missed Tim Wu writing on “The Attention Merchants.”]

Diagrams in Threat Modeling

When I think about how to threat model well, one of the elements that is most important is how much people need to keep in their heads, the cognitive load if you will.

In reading Charlie Stross’s blog post, “Writer, Interrupted” this paragraph really jumped out at me:

One thing that coding and writing fiction have in common is that both tasks require the participant to hold huge amounts of information in their head, in working memory. In the case of the programmer, they may be tracing a variable or function call through the context of a project distributed across many source files, and simultaneously maintaining awareness of whatever complex APIs the object of their attention is interacting with. In the case of the author, they may be holding a substantial chunk of the plot of a novel (or worse, an entire series) in their head, along with a model of the mental state of the character they’re focussing on, and a list of secondary protagonists, while attempting to ensure that the individual sentence they’re currently crafting is consistent with the rest of the body of work.

One of the reasons that I’m fond of diagrams is that they allow the threat modelers to migrate information out of their heads into a diagram, making room for thinking about threats.

Lately, I’ve been thinking a lot about threat modeling tools, including some pretty interesting tools for automated discovery of existing architecture from code. That’s pretty neat, and it dramatically cuts the cost of getting started. Reducing effort, or cost, is inherently good. Sometimes, the reduction in effort is an unalloyed good, that is, any tradeoffs are so dwarfed by benefits as to be unarguable. Sometimes, you lose things that might be worth keeping, either as a hobby like knitting or in the careful chef preparing a fine meal.

I think a lot about where drawing diagrams on a whiteboard falls. It has a cost, and that cost can be high. “Assemble a team of architect, developer, test lead, business analyst, operations and networking” reads one bit of advice. That’s a lot of people for a cross-functional meeting.

That meeting can be a great way to find disconnects in what people conceive of building. And there’s a difference between drawing a diagram and being handed a diagram. I want to draw that out a little bit and ask for your help in understanding the tradeoffs and when they might and might not be appropriate. (Gary McGraw is fond of saying that getting these people in a room and letting them argue is the most important step in “architectural risk analysis.” I think it’s tremendously valuable, and having structures, tools and methods to help them avoid ratholes and path dependency is a big win.)

So what are the advantages and disadvantages of each?

Whiteboard

  • Collaboration. Walking to the whiteboard and picking up a marker is far less intrusive than taking someone’s computer, or starting to edit a document in a shared tool.
  • Ease of use. A whiteboard is still easier than just about any other drawing tool.
  • Discovery of different perspective/belief. This is a little subtle. If I’m handed a diagram, I’m less likely to object. An objection may contain a critique of someone else’s work, it may be a conflict. As something is being drawn on a whiteboard, it seems easier to say “what about the debug interface?” (This ties back to Gary McGraw’s point.)
  • Storytelling. It is easier to tell a story standing next to a whiteboard than any tech I’ve used. A large whiteboard diagram is easy to point at. You’re not blocking the projector. You can easily edit as you’re talking.
  • Messy writing/what does that mean? We’ve all been there? Someone writes something in shorthand as a conversation is happening, and either you can’t read it or you can’t understand what was meant. Structured systems encourage writing a few more words, making things more tedious for everyone around.

Software Tools

  • Automatic analysis. Tools like the Microsoft Threat Modeling tool can give you a baseline set of threats to which you add detail. Structure is a tremendous aid to getting things done, and in threat modeling, it helps in answering “what could go wrong?”
  • Authority/decidedness/fixedness. This is the other side of the discovery coin. Sometimes, there are architectural answers, and those answers are reasonably fixed. For example, hardware accesses are mediated by the kernel, and filesystem and network are abstracted there. (More recent kernels offer filesystems in userland, but that change was discussed in detail.) Similarly, I’ve seen large, complex systems with overall architecture diagrams, and a change to these diagrams had to be discussed and approved in advance. If this is the case, then a fixed diagram, printed poster size and affixed to walls, can also be used in threat modeling meetings as a context diagram. No need to re-draw it as a DFD.
  • Photographs of whiteboards are hard to archive and search without further processing.
  • Photographs of whiteboards may imply that ‘this isn’t very important.” If you have a really strong culture of “just barely good enough” than this might not be the case, but if other documents are more structured or cared for, then photos of a whiteboard may carry a message.
  • Threat modeling only late. If you’re going to get architecture from code, then you may not think about it until the code is written. If you weren’t going to threat model anyway, then this is a win, but if there was a reasonable chance you were going to do the architectural analysis while there was a chance to change the architecture, software tools may take that away.

(Of course, there are apps that help you take images from a whiteboard and improve them, for example, Best iOS OCR Scanning Apps, which I’m ignoring for purposes of teasing things out a bit. Operationally, probably worth digging into.)

I’d love your thoughts: are there other advantages or disadvantages of a whiteboard or software?

The Evolution of Apple's Differential Privacy

Bruce Schneier comments on “Apple’s Differential Privacy:”

So while I applaud Apple for trying to improve privacy within its business models, I would like some more transparency and some more public scrutiny.

Do we know enough about what’s being done? No, and my bet is that Apple doesn’t know precisely what they’ll ship, and aren’t answering deep technical questions so that they don’t mis-speak. I know that when I was at Microsoft, details like that got adjusted as we learned from a bigger pile of real data from real customer use informed things. I saw some really interesting shifts surprisingly late in the dev cycle of various products.

I also want to challenge the way Matthew Green closes: “If Apple is going to collect significant amounts of new data from the devices that we depend on so much, we should really make sure they’re doing it right — rather than cheering them for Using Such Cool Ideas.”

But that is a false dichotomy, and would be silly even if it were not. It’s silly because we can’t be sure if they’re doing it right until after they ship it, and we can see the details. (And perhaps not even then.)

But even more important, the dichotomy is not “are they going to collect substantial data or not?” They are. The value organizations get from being able to observe their users is enormous. As product managers observe what A/B testing in their web properties means to the speed of product improvement, they want to bring that same ability to other platforms. Those that learn fastest will win, for the same reasons that first to market used to win.

Next, are they going to get it right on the first try? No. Almost guaranteed. Software, as we learned a long time ago, has bugs. As I discussed in “The Evolution of Secure Things:”

Its a matter of the pressures brought to bear on the designs of even what (we now see) as the very simplest technologies. It’s about the constant imperfection of products, and how engineering is a response to perceived imperfections. It’s about the chaotic real world from which progress emerges. In a sense, products are never perfected, but express tradeoffs between many pressures, like manufacturing techniques, available materials, and fashion in both superficial and deep ways.

Green (and Schneier) are right to be skeptical, and may even be right to be cynical. We should not lose sight of the fact that Apple is spending rare privacy engineering resources to do better than Microsoft. Near as I can tell, this is an impressive delivery on the commitment to be the company that respects your privacy, and I say that believing that there will be both bugs and design flaws in the implementation. Green has an impressive record of finding and calling Apple (and others) on such, and I’m optimistic he’ll have happy hunting.

In the meantime, we can, and should, cheer Apple for trying.

Sneak peeks at my new startup at RSA

Confusion

Many executives have been trying to solve the problem of connecting security to the business, and we’re excited about what we’re building to serve this important and unmet need. If you present security with an image like the one above, we may be able to help.

My new startup is getting ready to show our product to friends at RSA. We’re building tools for enterprise leaders to manage their security portfolios. What does that mean? By analogy, if you talk to a financial advisor, they have tools to help you see your total financial picture: assets and debts. They’ll help you break out assets into long term (like a home) or liquid investments (like stocks and bonds) and then further contextualize each as part of your portfolio. There hasn’t been an easy way to model and manage a portfolio of control investments, and we’re building the first.

If you’re interested, we have a few slots remaining for meetings in our suite at RSA! Drop me a line at [first]@[last].org, in a comment or reach out over linkedin.

Kale Caesar

According to the CBC: “McDonald’s kale salad has more calories than a Double Big Mac

NewImage

In a quest to reinvent its image, McDonald’s is on a health kick. But some of its nutrient-enhanced meals are actually comparable to junk food, say some health experts.

One of new kale salads has more calories, fat and sodium than a Double Big Mac.

Apparently, McDonalds is there not to braise kale, but to bury it in cheese and mayonnaise. And while that’s likely mighty tasty, it’s not healthy.

At a short-term level, this looks like good product management. Execs want salads on the menu? Someone’s being measured on sales of new salads, and loading them up with tasty, tasty fats. It’s effective at associating a desirable property of salad with the product.

Longer term, not so much. It breeds cynicism. It undercuts the ability of McDonalds to ever change its image, or to convince people that its food might be a healthy choice.

Open Letters to Security Vendors

John Masserini has a set of “open letters to security vendors” on Security Current.

Everyone involved in product or sales at a security startup should read them. John provides insight into what it’s like to be pitched by too many startups, and provides a level of transparency that’s sadly hard to find. Personally, I learned a great deal about what happens when you’re pitched while I was at a large company, and I can vouch for the realities he puts forth. The sooner you understand those realities and incorporate them into your thinking, the more successful we’ll all be.

After meeting with dozens of startups at Black Hat a few weeks ago, I’ve realized that the vast majority of the leaders of these new companies struggle to articulate the value their solutions bring to the enterprise.

Why does John’s advice make us all more successful? Because each organization that follows it moves towards a more efficient state, for themselves and for the folks who they’re pitching.

Getting more efficient means you waste less time per prospect. When you focus on qualified leads who care about the problem you’re working on, you get more sales per unit of time. What’s more, by not wasting the time of those who won’t buy, you free up their time for talking to those who might have something to provide them. (One banker I know said “I could hire someone full-time to reject startup pitches.” Think about what that means for your sales cycle for a moment.)

Go read “An Open Letter to Security Vendors” along with part 2 (why sales takes longer) and part 3 (the technology challenges most startups ignore).

The Evolution of Secure Things

One of the most interesting security books I’ve read in a while barely mentions computers or security. The book is Petroski’s The Evolution of Useful Things.

Evolution Of useful Things Book Cover

As the subtitle explains, the book discusses “How Everyday Artifacts – From Forks and Pins to Paper Clips and Zippers – Came to be as They are.”

The chapter on the fork is a fine example of the construction of the book.. The book traces its evolution from a two-tined tool useful for holding meat as it was cut to the 4 tines we have today. Petroski documents the many variants of forks which were created, and how each was created with reference to the perceived failings of previous designs. The first designs were useful for holding meat as you cut it, before transferring it to your mouth with the knife. Later designs were unable to hold peas, extract an oyster, cut pastry, or meet a variety of other goals that diners had. Those goals acted as evolutionary pressures, and drove innovators to create new forms of the fork.

Not speaking of the fork, but rather of newer devices, Petroski writes:

Why designers do not get things right the first time may be more understandable than excusable. Whether electronics designers pay less attention to how their devices will be operated, or whether their familiarity with the electronic guts of their own little monsters hardens them against these monsters’ facial expressions, there is a consensus among consumers and reflective critics like Donald Norman, who has characterized “usable design” as the “next competitive frontier,” that things seldom live up to their promise. Norman states flatly, “Warning labels and large instruction manuals are signs of failures, attempts to patch up problems that should have been avoided by proper design in the first place.” He is correct, of course, but how is it that designers have, almost to a person, been so myopic?

So what does this have to do with security?

(No, it’s not “stick a fork in it, it’s done fer.”)

Its a matter of the pressures brought to bear on the designs of even what (we now see) as the very simplest technologies. It’s about the constant imperfection of products, and how engineering is a response to perceived imperfections. It’s about the chaotic real world from which progress emerges. In a sense, products are never perfected, but express tradeoffs between many pressures, like manufacturing techniques, available materials, and fashion in both superficial and deep ways.

In security, we ask for perfection against an ill-defined and ever-growing list of hard-to-understand properties, such as “double-free safety.”

Computer security is in a process of moving from expressing “security” to expressing more precise goals, and the evolution of useful tools for finding, naming, and discussing vulnerabilities will help us express what we want in secure software.

The various manifestations of failure, as have been articulated in case studies throughout this book, provide the conceptual underpinning for understanding the evolving form of artifacts and the fabric of technology into which they are inextricably woven. It is clearly the perception of failure in existing technology that drives inventors, designers, and engineers to modify what others may find perfectly adequate, or at least usable. What constitutes failure and what improvement is not totally objective, for in the final analysis a considerable list of criteria, ranging from the functional to the aesthetic, from the economic to the moral, can come into play. Nevertheless, each criterion must be judged in a context of failure, which, though perhaps much easier than success to quantify, will always retain an aspect of subjectivity. The spectrum of subjectivity may appear to narrow to a band of objectivity within the confines of disciplinary discussion, but when a diversity of individuals and groups comes together to discuss criteria of success and failure, consensus can be an elusive state.

Even if you’ve previously read it, re-reading it from a infosec perspective is worthwhile. Highly recommended.

[As I was writing this, Ben Hughes wrote a closely related post on the practical importance of tradeoffs, “A Dockery of a Sham.”]