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One blessing of the Cybersecurity Executive Order

On May 12th, the Biden administration issued an Executive Order that was written to improve the overall security posture of software products that the government buys from the private sector. Recent events, such as the SolarWinds hack, contributed to the realization that such a move is necessary.

This Executive Order is a big deal. Of course, nothing will change overnight, but given the size and complexity of the software industry, as well as the overall culture behind software security (the culture of: “If the customer doesn’t see it — don’t spend money on it”), an Executive Order can probably yield the closest thing to immediate improvement that we could reasonably wish for. The US Government is a very large customer, and all major vendors will elect to comply with its requirements rather than cross it all off their addressable markets.

A lot has been written on how important it is for the government to use its buying power (if not its regulatory power) to drive vendors into shipping more secure products. Product security suffers from what could best be described as a market failure condition, which would call for such regulatory intervention.

To not overly repeat the mainstream media, I would like to focus on one unique aspect of the current Executive Order, and on how it can ignite a new trend that will change product and network security for the better. I’ll discuss true machine-readable security documentation.

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Recommended Podcast: Risky Business

I've been listening to the Risky Business security podcast for several years now, and mark it as among my favorite security podcasts, if not my favorite one. There are a few good security podcasts out there, but this is the one I listen to most rigorously, i.e., without missing an episode. Here is what makes this security podcast stand out.

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Book review: "Essentialism: The Disciplined Pursuit of Less"

The book Essentialism: The Disciplined Pursuit of Less, by Greg McKeown, carries a very important message: you shall not seek to do more, but rather to do less things, but do the ‘right’ ones. When people succeed in life (even moderate success), they are encouraged to do more and hence de-focus. In general, our society promotes the concept of doing more and more, which makes it hard for us to just say ‘no’ to additional commitments, even if those commitments invoke activities are not within our priorities. As Greg McKeown nicely puts it: if you don’t prioritize your life, someone else will.

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On the value of NFT

An NFT (Non-Fungible Token) is a data structure that points at a particular data object in a unique way. See it as a way of naming digital objects, such as photos, texts, audio or video, in a way that allows referring to them with no ambiguity.

The ability to refer to data objects allows to “mention” them in transactions. This seemingly trivial ability, when combined with the ability to create immutable records of transactions (as provided by Blockchains), allows us to create immutable records that refer to data objects.

Technically, NFTs do not require blockchains. You could take a photo of a cat, create an NFT for this photo, which is essentially a unique pointer to (or: a descriptor of) it, and then go on to write a real contract on paper that says “this photo of a cat, bearing this unique ID, is hereby assigned to John Smith”, whatever this assignment means.

Blockchains and smart contract technologies allow for such digital agreements to be stored in a public immutable record that does not allow anyone to change it once it was written. The combination of NFTs and blockchain-based smart contracts thus allows us to securely record agreements that declare ownership of digital goods. If you have any file (photo, text, video, etc.), you can create an attestation that tells the entire world that you assign this file to be owned by whoever. What does this “ownership” mean? – Good question; but whatever it means, billions of dollars have already been paid towards such ownerships. Is this real? The money surely is, but is also the value?

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COVID vaccination certificates done almost right

Israel is probably the most advanced to date in terms of COVID19 vaccination. With more than one third of the residents fully inoculated, life can almost get back to pseudo-normal. This, however, requires being able to tell the vaccinated people apart from those who are not. The green pass, or vaccination certificate, is made to achieve precisely that. Technically, this government-issued certificate is not substantially different than a driver’s license, just that it’s shorter lived, can be stored in a phone app, and most importantly: was designed in a hurry.

For something that was launched so quickly, it seems to be decently architected, but slightly better work could still be done to protect that piece of attestation that is so critical to public health.

What do we require of a vaccination certificate? Not much, really. It obviously needs to be as secure as it could be made under the strict cost and distribution constraints. The certificate has to also be easily renewable (it currently expires every six months), and it has to be verifiable by a wide range of checkpoints with varying capabilities. Finally, verification has to be both reliable and fast; entry into a shopping mall cannot resemble passport control, and people cannot arbitrarily be locked out of key facilities just because of simple IT downtime.

The certificate itself is sent to its holder by e-mail (or via a web-site), to be printed at home. There are no measures that could be taken to prevent anyone with Microsoft Paint from crafting fake such certificates. The digital part of the vaccination certificate, i.e., the QR Code printed on it, is the only part of the certificate that can practically be used against forgery.

See the following write-up as a quick guide to cheap-but-secure attestation certificates; for COVID or otherwise.

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The role of security focused alternatives

Our digital lives are more or less governed by very few providers of products and services. Our desktop computing is almost invariably based on Microsoft Windows, our document collaboration is most likely based on either Google Docs or on O365, our instant messaging is either Whatsapp or Slack, our video collaboration is either Teams or Zoom, etc. Given the prevalence of digital life and work, you would expect more options to exist. However, all those large pies seem to each be divided into just a few thick slices each. Those lucky providers that won their dominance did so by catering to the needs of the masses while serving their own agendas, or more accurately: by serving their own agendas while giving enough to make their products preferable by the masses.

Customers appreciate ease of deployment and ease of use, and all of the dominant products excel in that. However, customers never said anything too explicit about security and customers never demanded data sovereignty. Those properties are also very non-compelling for some providers, either because they increase cost, because they prevent lock-in, or because they hinder business models that rely on using customer data. The vast majority of customers never really required, and hence never really got, anything more than ease of use and ease of deployment, along a few key functional features. For most customers, this is enough, but customers who also require security, privacy, and/or data sovereignty, face a challenge when working out alternatives.

But alternatives do exist, for desktop computing, for collaboration and for messaging and video communication. Those alternatives play an important role in our digital ecosystem, even if most people never care to use them.

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TEDTalk: "The counterintuitive way to be more persuasive"

This is a brilliant TED Talk by Niro Sivanathan.

It introduces the dilution effect. Information that is less relevant is not merely discarded, but rather dilutes the impact of the information that is relevant. So next time you bring up arguments for something, remember that your arguments don’t add up – they average out.

TEDTalk: The counterintuitive way to be more persuasive

Machine Learning Security: a new crop of technologies

Artificial Intelligence (AI), and Machine Learning (ML) specifically, are now at the stage in which we start caring about their security implications. Why now? Because that’s the point at which we usually start caring about the security considerations of new technologies we’ve started using. Looking at previous cases, such as of desktop computing, the Internet, car networks, and IoT (Internet of Things), those technologies first gained fast momentum by the urge to capitalize on their novel use-cases. They were deployed as fast as they could possibly be, by stakeholders rushing to secure their share of the emerging revenue pie. Once the systems started operating en masse, it was finally time to realize that where there is value – there is also malice, and every technology that processes an asset (valuable data that can be traded, the ability to display content to a user and grab her attention, potential for extortion money, etc.) will inevitably lure threat actors who demonstrate impressive creativity when attempting to divert or exploit those assets.

This flow of events is barely surprising, and we were not really shocked to learn that the Internet does not provide much security out of the box, that cars could be hacked remotely through their wireless interfaces, or that cheap home automation gear doesn’t bother to encrypt its traffic. This is economy, and unless there is an immediate public safety issue causing the regulator to intervene (often later than it should), we act upon security considerations only once the new technology is deployed, and the security risks are manifested in a way that they can no longer be ignored.

It happened with desktop computing in the 80’s, with the Internet in the 90’s, with car networks about a decade ago, and with mass IoT about half a decade ago. (In those approximate dates I am not referring to when the first security advocate indicated that there are threats, this usually happened right away if not before, but to when enough security awareness was built for the industry to commit resources towards mitigating some of those threats.) Finally, it’s now the turn of Machine Learning.

When we decide that a new technology “needs security” we look at the threats and see how we can address them. At this point, we usually divide into two camps:

  • Some players, such as those heavily invested in securing the new technology, and consultants keen on capitalizing on the new class of fear that the industry just brought on itself, assert that “this is something different”; everything we knew about security has to be re-learned, and all tools and methodologies that we’ve built no longer suffice. In short, the sky is falling and we’re for the rescue.

  • Older security folks will point at the similarities, concluding that it’s the same security, just with different assets, requirements, and constraints that need to be accounted for. IoT Security is the same security just with resource constrained devices, physical assets, long device lifetime, and harsh network conditions; car security is the same security with a different type of network, different latency requirements, and devastating kinetic effects in case of failure, and so forth.

I usually associate with the second camp. Each new area of security introduces a lot of engineering work, but the basic paradigms remain intact. It’s all about securing computer systems, just with different properties. Those different properties make tremendous differences, and call for different specializations, but the principles of security governance, and even the nature of the high-level objectives, are largely reusable.

With Machine Learning the situation is different. This is a new flavor of security that calls for a new crop of technologies and startups that deploy a different mindset towards solving a new set of security challenges; including challenges that are not at focus in other domains. The remainder of this post will delve into why ML Security is different (unlike the previous examples), and what our next steps could look like when investing in mitigation technologies.

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Book review: "Think Like a Rocket Scientist"

The book “Think Like a Rocket Scientist” by Ozan Varol (a real rocket scientist, actually), has nothing to do with Security. However, I do have the habit of sharing recommendations on such resources as well, and this piece is certainly worthy of such a recommendation.

The text promotes the deployment of thought processes that are often used in engineering and science (primarily in rocket science, where mistakes are costly), by everyone. The motivation of this book is probably a quote brought by Carl Sagan: “Science is a way of thinking much more than it is a body of knowledge”; a statement with which I could not agree more.

The book covers a few principles and delves into each one of them with excellent examples and historic facts, all written in an engaging style. Some of the topics that the author discusses are:

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Product Security Governance: Why and How

The term “security governance” is not widely used in the product security context. When web-searching for a decent definition, among the first results is a definition by Gartner that addresses cyber security rather than product security. Other sources I looked at also focus on IT and cyber security.

But product security governance does exist in practice, and where it doesn’t – it often should. Companies that develop products that have security considerations do engage in some sort of product security activities: code reviews, pen-tests, etc.; just the “governance” part is often missing.

Product security is science; treat it as such.

This post describes what I think “security governance” means in the context of product security. It presents a simple definition, a discussion on why it is an insanely important part of product security, and a short list of what “security governance” should consist of in practice.

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