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One of the common misconceptions I encounter when explaining threat modeling to people is the is of operating system scale. This is one of those cases where size really does matter.

When threat modeling, there is a desire to do as little work as possible. By that I mean that you shouldn’t model the same thing multiple times. Model it once put it in a box and move on. It’s furniture.

We do this to allow us to focus on the stuff we’re developing and not third-party or open source bits.

When it comes to operating systems, however, I don’t have just one border to deal with as I would with say a vendor provided driver. The thing we casually refer to as an operating system is actually a many layered beast and should be treated as such. When we do, the issue of OS scale disappears in a puff of abstraction smoke.

So, what is this so far unexplained scale?

Let’s rewind a bit to the original computers. They were slow, small (computationally and with respect to storage) and, in the grand scheme of things, pretty simple. There was no operating system. The computer executed a single program. The program was responsible for all operational aspects of its existence.

As computers became more sophisticated, libraries were created to provide standardized components, allowing developers to focus on the core application and not the plumbing. Two of these libraries stand out: the mass storage and communications libraries. We would eventually refer to these as the file system and network.

When computers began expanding their scope and user base, the need for a mechanism to handle first sequential, then later multiple jobs led to the development of a scheduling, queueing and general task management suite.

By the time Unix was introduced, this task manager was surrounded by access management, program development tools, general utilities and games. Because, well, games.

For users of these systems, the OS became shorthand for “stuff we didn’t need to write.”

The odd thing is that, on the periphery, there existed a class of systems too small or too specialized to use of even require this on-stop-shopping OS. These were the embedded systems. For decades, these purpose-built computers ran one program. They included everything from thermostats to digital thermometers. (And yes, those Casio watches with the calculator built in.)

Over time, processors got a lot more powerful and a lot smaller. The combination of which made it possible to run those previously resource hungry desktop class operating systems in a little tiny box.

But what happens when you want to optimize for power and space? You strip the operating systems down to their base elements and only use the ones you need.

This is where our OS sizing comes from.

I like to broadly divide operating systems into four classes:

  • bare metal
  • static library
  • RTOS
  • desktop / server

Each of these presents unique issues when threat modeling. Let’s look at each in turn.

Bare Metal

Probably the easiest OS level to threat model is bare metal. Since there’s nothing from third-party sources, Development teams should be able to easily investigate and explain how potential threats are managed.

Static Library

I consider this the most difficult level. Typically, the OS vendor provides sources which development builds into their system. Questions of OS library modification, testing specific to target / tool chain combination, and threat model (OS) arise. The boundaries can become really muddy. One nice thing is that the only OS elements are the ones explicitly included. Additionally, you can typically exclude aspects of the libraries you don’t use. Doing so, however, breaks the de-risking boundary as the OS vendor probably didn’t test your pared down version.

RTOS

An RTOS tends to be an easier level than a desktop / server one. This is because the OS has been stripped down and tuned for performance and space. As such, bits which would otherwise be laying about for an attacker to leverage are out of play. This OS type may present issues in modeling as unique behaviors may present themselves.

Desktop / Server

This is the convention center of operating systems. Anything and everything that anyone has ever used or asked for may be, and probably is, available. This is generally a bad thing. On the upside, this level tends to provide sophisticated access control mechanisms. On the downside, meshing said mechanisms with other peoples’ systems isn’t always straightforward. In the area of configuration, as this is provided by the OS vendor, it’s pretty safe to assume that any configuration-driven custom version is tested by the vendor.

OS and Threat Modeling

When threat modeling, I take the approach of treating the OS as a collection of services. Doing so, the issue of OS level goes away. I can visually decompose the system into logical data flows into process, file system and network services; rather than a generic OS object. It also lets me put OS-provided drivers on the periphery, more closely modeling the physicality of the system.

It’s important to note that this approach requires that I create multiple threat model diagrams representing various levels of data abstraction. Generally speaking, the OS is only present at the lowest level. As we move up the abstraction tree, the OS goes away and only the data flow between the entities and resources which the OS was intermediating will be present.

Let’s consider an application communicating via a custom protocol. At the lowest level, the network manages TCP/UDP traffic. We need to ensure that these are handled properly as the transit the OS network service. At the next level we have the management of the custom protocol itself. In order to model this properly, we need for the network service to not be involved in the discussions. Finally, at the software level, we consider how the payload is managed (let’s presume that it’s a command protocol).

Nowhere in the above example does the OS level have any impact on how the system would be modeled. By decomposing the OS into services and treating layers uniformly, we gain the ability to treat any OS like furniture. It’s there, but once you’ve established that it behaves properly, you move on.

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There have been numerous times when a new technology has led to a major shift in how we thought about how computers and software should be built. We are about to see one of those shifts. At least that’s what I’ve come to believe.

Let’s pop into the Wayback and set our sights on the early ’80s. At that time computers had one processor. Hardware-based floating point were the domain of mainframes and minicomputers. Communications between computers existed only for the well-heeled. Security meant keeping your computer locked up.

Life was pretty simple. If you wanted something done, you did it yourself. When software was shared it was done via the US Postal Service on 9-track tape.

Fast forward to the early ’90s. Desktop computers were fairly common. Uniprocessors still ruled. Hardware floating point was now readily available. The internet had just been introduced. Gopher was slowly to be displaced by the combination of FTP and web search engines. Security issues were a thing that happened, but was, on the whole a black art practiced by a small number of individuals and required skills that you needed to develop yourself.

It was around this time that I was casting about for a thesis topic for my Master’s in Electrical and Computer Engineering. I took on the topic of virus-resistant computer architectures (AARDVARK). Did I mention that it was 1992? Just researching the state of the art in computer viruses was a huge task. No Google, Amazon or ACM online article search. As to the other side of the equation, the how and why of hacking, well, I’ll leave that for another time.

By the time I was done, I’d proposed a computer architecture with separate instruction and data spaces where the application’s binary was encrypted and the key loaded in a separate boot sequence was stored in a secure enclave, accessible only to the binary segment loader. Programs were validated at runtime. I conjectured that such a computer would be ideal for secure use and could be built within the 18 months.

Everyone thought it was a great design and the school even worked with me to apply for a patent. The US Patent Office at that time didn’t get it. After five years we abandoned the effort. I was disappointed, but didn’t lose sleep over it.

Fast forward to 2012 when Apple released the iOS 6 security guidelines. Imagine my amusement when I see echos of AARDVARK. It’s all there: signed binaries, secure enclave, load validation. Good on them for doing it right.

Let’s step back and consider the situation. Computers are really small. They have integrated hardware floating point units, multi-processors and now, with the advent of this generation of iPhone, hardware-based security. The internet has gone global. Google indexes everything, Open source is a thing. So, we’re good?

Not so much. The Apple iPhones are an oasis in a vast desert of security badness. Yes, IPv6 has security goodness available, but IPv4 still rules. Secure programming practices are all but non-existent. Scan and contain is the IT mantra. Threat modeling is an exercise for the academic.

This brings to last year. Microsoft announced Azure Sphere. Application processor, dual-MCU, networking processor, security processor. All firewalled. All in the same package. The provided OS was a secured version of Linux. Each device is registered so only the manufacturer can deploy software, push updates and collect telemetry via the Azure cloud.

There must be a catch. Well, as you know, there’s no such thing as a free burrito.

The first device created to the Azure Sphere specification is the Mediatek MT3620. And no, you can’t use it for your next laptop. The target is IoT. But, there’s a lot of horsepower in there. And there’s a lot of security and communications architecture that developers won’t have to build themselves.

Microsoft is touting this a the first generation. Since they started with Linux and ARM, why wouldn’t you want to get something with more power for systems that have security at their core. If Microsoft approached this as Apple has the iPhone, iPad, AppleTV and Apple Watch; why shouldn’t we expect consumer computers that aren’t insecure.

But will I be able to use them for software development? That’s a tricky question.

When I envisioned AARDVARK, my answer was no. That architecture was designed for end-user systems like banks and the military. You can debug a Sphere device from within Visual Studio, so, maybe it’s doable. You’d need to address the issue of a non-isomorphic ownership model.

Are users willing to bind their device to a single entity? Before you say no, consider how much we’re already put in the hands of the Googles and Facebooks of the world. Like it or not, those are platforms. As are all the gaming systems.

Regardless, I believe that we will end up with consumer compute devices based on this architecture. Until then we’ll just have to watch to see whether the IoT sector gets it and by extension the big boys.

Either way, the future is Sphere.

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Like many people working in technology, every year I assemble a summary of accomplishments for my annual review. It’s always interesting to take a long view look at things. This is especially true when you’re working on things with long lifetimes and uncertain outcomes.

Let’s look at the raw numbers:

  • 73 topics researched
  • 1 intern supervised / mentored
  • 11 independent projects led
  • 2 classes created
  • 3 classes updated
  • 8 classes taught
  • 61 student taught
  • 6 books reviewed for possible internal use
  • 2 standards bodies participated in
  • 1 ISO technical study group chaired
  • 18 project teams worked with
  • 12 first-line managers worked with
  • 4 upper-level managers worked with
  • multiple outside organizations worked with
  • 126 individual code reviews participated in
  • 29 internal trainings taken
  • 2 conferences attended
  • 2 Coursera classes taken

How can I be sure about these numbers? In a word, notebooks. I’m old school that way. It’s not that I don’t use technology for notes. I use Microsoft OneNote to track various subjects and lines of thought. URLs don’t really work in notebooks (or notes to Santa). But for daily tracking of thoughts and events, being able to pick up a pen and start writing is unequaled for me. I take my notebook to meetings preferentially. They don’t get IMs in the middle of meetings.

From these notebooks come my weekly status summaries. From those come my annual status summary document. If anything, my numbers may be a little low. There are times when I neglect to write in my notebooks.

2018 was a very busy year. Lots of overlapping projects in flight. Many of which produced their first fruits. Most of them were multiple years in planning and execution, requiring the efforts across many teams. I love it when a plan comes together. I had the opportunity to work with a cosmic boat load of teams.

It’s a pleasure for me to be asked to create and teach classes. I learn more about the subjects. I get to help improve other peoples’ skills.

Participating in conversations with the ISO C / C++ committees is always an education for me. It doesn’t matter how long I worked with a programming language, there’s something to learn, some new view or example that will help me teach others. It fun.

Participating in code review is much along the same lines. It’s a discussion with the code and the developer. Done properly, everyone learns something. And through the process, you get better code.

I read a lot, but usually that’s a person endeavor. Reading technical books for possible use by others within my organization either for general reference of in conjunction with a class is a different kind of reading. The scope, presumed background and audience are all very different from me just adding another chunk into my existing world map. I need books in support of individuals who need current reference materials. Sometimes I need them in support of technology which is vastly out of date.

One thing I don’t track are all the non-dead tree reference materials I review, summarize and pass along in support of the research projects or management requests for information that I do on a daily basis. On one level, this is a hole in my somewhat obsessive self tracking. On the other, doing so would be too much of an interruption to flow. This material, at least the good stuff, get tracked by subject in OneNote. Eventually, it’s either folded into supporting material summaries for management or put at the end of class material sections of for support and further reading.

Some of my most interesting work last year revolved around interactions with outside organization. Bringing technologies in to lighten the load and support group efforts is always satisfying.

As to what 2019 will be like, who can say. If it’s anything like 2018 was, there will be a lot to write about in next year’s review post.


image credit: Dustin Liebenow (creative commons)

 

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When I was an undergraduate, I heard a story about a DEC PDP 11/70 at a nearby school that had a strange hardware mod. A toggle switch had been added by someone and wired into the backplane apparently. The switch had two settings, “magic” and “more magic.” The identity of the individual or individuals having made the mod was lost. For as long as anyone could remember, the switch had been in the “magic” position. One day, some brave soul decided to find out what happened when the “more magic” setting was selected. Upon flipping the toggle, the machine crashed. It thereafter resisted any attempts to get it to run. After a bit, they gave up, flipped the toggle back to “magic“, power cycled the machine and hoped. The machine returned to its previous state of operational happiness. One could say that they’d been trying to achieve too much of a good thing.

We might read this and come away with the idea that, well, they just should have gotten on with their work and not worried about the plumbing. That’s certainly the majority view, from what I’ve seen. But why was the switch there in the first place? If it only worked in one position, shouldn’t they have just wired things without the switch?

Let’s consider the temporal aspect, no one remembered who, when, or why, let alone what. It may well have been the case that once “more magic” actually worked. Who can say. That whole documentation thing.

When I work with project teams and individual developers, I have a habit of saying “no magic.” It comes from having heard this story. I’ll say this when working with individuals who’s code I’m reviewing, teams who’s architecture I’m reviewing, or leads and architects while facilitating the creation of threat models. I don’t care whether the magic manifests as constants (magic numbers) from who-knows-where or logic that’s more convoluted than a Gordian Knot. Basically, if the reason that something is being used exists without the benefit of understanding, it shouldn’t be there. I don’t care who put it there or how smart they were. Someday someone is going to come along and try to change things and it will all go south. Enough of these in a code review and it’s likely to get a single summary review comment of “no.”

How does this relate to security? Do you know what the auto-completion for “never implement your” is? I’ll let you try that on you own, just to verify. Never implement your own crypto[graphic functions]. Why? Where should I start? The math is torturous. The implementation is damn hard to do right. Did you know that you can break poorly implement crypto via timing analysis? Even if you don’t roll your own crypto, are you using some open source library or the one from the operating system? Do you know when to use which? Are you storing your keys properly?

Magic, all of it.

Some people believe that security can be achieved by obscuring things. These also tend to be the same people who’ve never used a decompiler. You’d be amazed what can be achieved with “a lot of tape and a little patience.”

If your goal is to have software and systems that are secure, you can’t have magic. Ever.

So, when I see company with a core philosophy of “move fast, break things,” I think well aren’t they going to have more attack surface than a two pound chunk of activated carbon. Not amazingly, they did and we are worse off because of it.

You can’t secure software-based systems unless you understand how the pieces play together. You can’t understand how the pieces play together until you understand how each piece behaves. You can’t understand how a piece behaves if it’s got magic floating around in it. It’s also important to not just glom onto a technique or technology because it’s handy or trendy. As Brian Kernighan and P.J. Plauger said, “it is dangerous to believe that blind application of any particular technique will lead to good programs[2].”

While you’re out there moving fast and tossing things over the wall, keep in mind that someone else, moving equally fast, is stitching them together with other bits. The result of which will also be tossed over another wall. And while it is true that some combination of these bits product interesting and quite useful results, what is the totality of their impact? At what point are we simply trusting that the pieces we’re using are not only correct and appropriate to our use, but don’t have any unintended consequences when combined in the way we have done.

You need to know that every part does the thing you intend it to do. That it does it correctly. And that, it does nothing you don’t intend. Otherwise, you’re going to have problems.

I’ll close with another story. In the dim days, before people could use the Internet (big I), there were a number of networks. These were eventually interconnected hence the name interconnected networks or Internet for short. Anyway, back in the day (early ’80s), universities were attaching to the Internet backbone, which was in and of itself pretty normal. What was not normal was when someone accidentally mounted a chunk of the Andrews File System (AFS) onto an Internet node. It ended up mounting the entirety of AFS on the Internet. This had the unexpected side effect of making a vast number of students previously unprotected emails publicly available to anyone with Internet access. Mostly that meant other university students. AFS wasn’t actually designed to be connected to anything else at that time. Bit of a scandal.

Unintended consequences.


  1. Image credit: Magic Book By Colgreyis ©Creative Commons Attribution 3.0 License.
  2. Kernighan and Plauger, Software Tools, 1976, page 2 paragraph 4

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When creating a class, it’s important to have a motivating example. In my experience, people learn best when they can see an immediate application to their own work. In the area of cybersecurity, this can be difficult. Examples in this space tend to be either too esoteric (return-oriented programming) or too divorced from the domain (credit card theft).

I’ve just finished up the creation of a two hour software security fundamentals class for management and developers. This is intended to provide a framework for integrating security into the software development process. Build it in vs. bolt it on. As I was putting the class together, the motivating example was just out of reach.

The push-back that must be overcome is that there already exists a process for dealing with security issues. It’s an extension to the standard quality assurance process. This process merely needs to be extended to include security-related testing, right?

Let’s look at that assertion for a moment.

How exactly does quality assurance work? Well, it’s based, by and large, on the flawed hypothesis model. Starting with the documentation, test cases are created to verify the assertions made therein. From there, scenarios are imagined. These are likewise verified. If issues (bugs) are discovered, generalizations are attempted. Any found point to larger problems in the code.

Sounds good, what’s the problem?

Consider the internet joke:

QA engineer walks into a bar. They order a beer, then order 0 beers, then order 999999999 beers, then orders a lizard, then orders -1 beers, then orders a eawlirensadk.

A customer walks into the bar and asks where the bathroom is. The bar bursts into flames, killing everyone.

That’s pretty much the problem with the flawed hypothesis model. You only verify the things you think of. If you’re only looking at how the bar serves beer, you’ll never catch issues involving other aspects of the system (here, bathroom location).

It’s a bit extreme as a motivating example, but everyone can relate to it, which is, of course, the point.

From there, the concept of flaws vs. bugs can emerge. QA finds bugs. On a good day, these may point to flaws. So, what’s the difference. For the purposes of discussion, flaws are design defects and bugs are implementation (code) defects. By its very nature, QA does not test design, only implementation.

At this point, management asks the question, isn’t this how it’s always been? Generally speaking, yes. Long gone are the days when people used program design language (PDL) to reason about the soundness of their software. At that time, security wasn’t much of a focus.

Enter threat modeling. By its very nature, threat modeling allows us to reason on the design. Why? Because it focuses not on the documentation, but rather the data flows and through extension the work flows of the system. Because we abstract ourselves from the implementation, we can reason about the system in ways that point us directly to security flaws.

To relate the impact to the real world, one has only to look at the cost to Samsung of not catching a design flaw in the Note 7 prior to release (US$17B). IBM estimates that relative to catching an issue a the design stage that the cost is 6.5 time higher in the implementation stage, 15 time higher during testing, and 100 times higher after release.

I’m in no way advocating the elimination of QA testing. You need both. As well as the processes we do in between, such as code reviews and static / dynamic analysis. But again, discovering issues in these stages of development is going to be more expensive. Defense-in-depth will always give you a better result. This is true not only in security, but the development process itself.

As I was finishing up my software security fundamentals class, the news broke regarding a high-profile technology firm that exposed the private data (images) of millions of individuals via their developer APIs. This is probably a case of failing to threat model their system. This isn’t the first time that this particular company has failed miserably in the area of security. It points out, in ways which greatly assist my efforts to get management on-board, that the failed hypothesis model is no substitute for critical analysis of the design itself.

As a system grows in complexity, it is critical to abstract out the minutiae and let the data flows point toward possible issues in the design. Threat modeling is one technique, but not the only one, that makes that possible.

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I get called upon to do fairly incongruous things. One day it’ll be C++ usage recommendations. Another will find me preparing background materials for upper management. Some days, I’m prototyping. Always something new.

As of late, I’ve been bringing modern software threat modeling to the development teams. Threat modeling is one of those things that, for the most part, exists only in the realm of the mythical cybersecurity professionals. This is a sad thing. I’m doing what I can to change people’s perceptions in that regard.

Within cybersecurity, there is a saying. “You can either build it in or bolt it on.” As with mechanical systems, bolting stuff on guarantees a weak point and a usually lack of symmetry. From the software development standpoint, attempting to add security after the fact is usually a punishing task. It is both invasive and time-consuming.

But the bolt-on world is the natural response for those who use of the flawed hypothesis model of cybersecurity analysis. The appeal of the flawed hypothesis analysis lies in the fact that you can do it without much more than the finished product and its documentation. You can poke and prod the software based on possible threats that the documentation points toward. From the specific anticipated threats one can generalize and then test. The problem is that this methodology is only as good as the documentation, intuition, and experience of those doing the analysis.

So, what’s a software development organization to do?

Enter threat modeling. Instead of lagging the development, you lead it. Instead of attacking the product, you reason about its data flow abstraction. In doing so, you learn about how your design decisions impact your susceptibility to attack. From there, you can quantify the risk associated with any possible threats and make reasoned decisions as to what things need to be addressed and in what order. Pretty much the polar opposite of the “death by a thousand cuts” approach of the flaw hypothesis model.

Sounds reasonable, but how do we get there?

Let me start by saying that you don’t create a threat model. You create a whole pile of threat models. These models represent various levels of resolution into your system. While it is true that you could probably create an über threat model (one to rule them all, and such), you’d end up with the graphical equivalent of the Julia Set. What I’ve found much more manageable is a collection of models representing various aspects of a system.

Since the 1970’s, we’ve had the very tool which we’ll use to create our models. That tool is the data flow diagram. The really cool thing about DFD’s is that they consist of just four components. In order to adapt them to threat modeling we need add only one more. The most important piece it the data store. After all, there’s not much to look at in a computer system that doesn’t actually handle some sort of data. We manipulate the data via processes. These agents act upon the data which moves via flows. And finally we need external actors, because if the data just churns inside the computer, again, not much interest. That’s it. You can fully describe any system using only those four primitives.

Okay, you can describe the system, but how does this relate the threat modeling? To make the leap from DFD to threat model, we’ll need one more primitive. We need a way to designate boundaries that data flows cross. These we call threat boundaries. Not the world’s most imaginative nomenclature, but hey, it’s simple and easy to learn.

Once we have a particular DFD based on a workflow, we add boundaries where they make sense. Between the physical device and the outside world; or the application and operating system; or application and its libraries; or between two processes; or … (you get the idea). Again the threat model isn’t intended to be the application. It’s an abstraction. And as Box said, “all models are wrong … but some are useful.” It helps to keep in mind what Alfred Korzybski said, “the map is not the territory.” Anyone who’s traveled on a modern transit system would never confuse the transit map for the area geography. Nod to Harry Beck.

With the boundary-enhanced DFD, we can get to work. For the particular road I travel, we reason about the threat model using a STRIDE analysis. We consider each of the elements astride (pun) each data flow with respect to each of the six aspects of STRIDE: spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege. Not all aspects apply to all combination of our four primitives. There are tables for that. Each of these can be appraised logically. No chicken entrails required. At the end of the day, you have a collection of things you don’t have answers to. So, you bring in the subject matter experts (SMEs) to answer them. When you are done what remains are threats.

Threats. Spiffy. But not all threats are equal. Not in potential for damage, or likelihood, or interest. For a goodly length of time, this was a big problem with the whole threat modeling proposition. Lots of stuff, but no objective way to triage it.

Enter the Common Vulnerability Scoring System (CVSS). This is the Veg-O-Matic of threat risk quantification. CVSS considers the means, complexity, temporality and impact areas of a threat. From these it computes a vulnerability score. Now you have a ranking of what the most important things to consider are.

For many industries, we could stop right here and use the CVSS score directly. No so in the land of FDA regulation. For that land adds another dimension, patient safety (PS) impact. The augmented CVSS-PS ranking guides us to a proper way to objectively rate the threats from most to least severe.

Now, we can take these ranked threats and present them, complete with SME feedback, to the core and risk teams for determination and disposition.

But we’re really not done. The threat modeling process isn’t one-and-done. We take what we learn and build it into the base assumptions of future products. Once the product is built, we compare it to the original threat model to verify that the model still represents reality.

Well, that was a lot of exposition. Where’s the facilitation and teaching?

Actually, the exposition was the teaching. And the teaching was and explanation of how I go about facilitating.

When all is said and done, a threat model needs to be built in. That is, engineering owns it. The whole facilitation thing, that’s a skill. It needs to live in the teams, not in some adjunct cybersecurity group. Applying CVSS consistently takes a bit of practice, but again we’re back to facilitation.

As to actually teaching threat modeling, that takes the better part of a day. Lots of decomposition, details and diagrams. I like teaching. It’s a kind of cognitive spreading of the wealth. The same is true of facilitation, just more one-to-one.

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The book Creative Selection by Ken Kocienda was recommended to me. This is unusual in that I’m typically the one recommending books to others.

This book follows the creation of the iPhone as seen through the eyes of the author, who was a software developer at Apple at the time. There are many interesting aspects to his story, to his interactions with the movers and shakers within Apple including eventually with Steve Jobs, and to the creative dynamic that made the iPhone possible at all.

Over the years, I’ve had the opportunity to participate in the world of Apple’s alpha and beta hardware, as well as that of many other technology firms as an engineer at firms working with Apple. I’ve also been on the other side of the fence, designing, developing and managing hardware and software, and their associated beta programs. It’s a challenging environment.

I’ve also had the opportunity of presenting my work to those holding the levers of power who had, shall we say, a less than gentle manner of showing disapproval with those thing not meeting with their standard of quality. There is an interesting combination of exhilaration and dread surrounding such presentations.

Elaborating on the details of the book in this post wouldn’t serve to encourage the would-be reader. If you are a follower of the history of Apple or technology, you’ll find things that will expand your view of the period and the dynamics surrounding it. You should also come away with a bit of insight into how Jobs looked at features. At least within the scope of the work that Kocienda presented him.

As a first outing, Kocienda does a reasonable job of painting a picture of the time and place that was Apple before they changed the world of mobile phones. The book is illustrated, which is unusual in a day and age of contemporaneous photography. I can’t imagine there not being numerous of photos from the proto-iPhone during its gestation. The pace of the book is decent and he conveys a sense of presence in the narrative. I felt that it could have been tighter, although that would have reduced the already roughly 200 pages of prose even further.

All-in-all, the book is well supported by references and is approachable by those not of the software tribe. You should be able to dispatch this one in a few hours. It’ll be joining my other volumes on the history of technology of this period.

 

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