Thoughts about the GDPR

I’ve been a bit of an outspoken critic of the GDPR on Twitter, Mastodon, and on the DefSec podcast.  I’m frequently asked to describe my issues with the regulation, and usually leave my view summarized as “it’s just a poorly written regulation”.

Many of my contacts, particularly those in the EU, do not understand the hoopla leading up to the May 25, 2018 enforcement date.  The prevailing view is that the GDPR mandates a few simple things, such as compelling me to:

  • explain why I am collecting your personal data
  • show you the personal data that I’ve collected about you
  • delete your personal data when I no longer need it
  • protect the personal data I have about you using things like encryption
  • obtain your explicit permission prior to doing anything with your personal data

Those seem like noble goals, and if that’s all the GDPR did, I would not be so critical.  But as with most things in life, the devil is in the details.  The GDPR spans 99 articles and, in total, weighs in at nearly 55,000 words.  Despite the voluminous nature of the regulation, the data protection regulators of the various EU countries formed a Working Party to produce a substantial number of guidance documents on how the GDPR will be implemented an enforced, since the regulation was not specific in these areas.  Also, some individual regulators, such as France’s CNIL, have been releasing still more guidance.

With that as a background, I’ll go into my specific issues with the GDPR:

  1. Most of the consternation in the IT world regarding the GDPR relates to Article 32, which defines the requirements for security and integrity of data processing.  Article 32 is intentionally vague in terms of the protections that need to be applied.  This is almost certainly in part to ensure the regulation is technology agnostic and does not become out of date as technology and threats evolve.  No doubt it’s also due to the fact that companies really don’t like to be told how to run their IT, at the level of specific controls and technologies.  There is a major disconnect between the regulation, the realities of running a business who needs to process personal data, and the realities of the threat landscape.  Perfect data security is an illusion, and breaches will happen.  Many organizations are looking down the barrel of a gun that has bullets that will poke 4% holes in their revenue, hoping the one pointed at them isn’t the one that gets fired.  The GDPR does not provide a safe harbor for organizations handling personal data.  By that I mean that the regulation doesn’t enumerate a certain set of controls that, if in place and operating correctly, would reduce or eliminate the threat of the massive potential fines.  So, organizations have to prioritize their security investments and improvements based on their understanding of the threat landscape.  But, we have plenty of objective evidence that demonstrates many organizations can’t do this effectively.  The GDPR does encourage the creation of a certification standard, but that has not come to fruition.  Many organizations seem to be latching on to ISO27001/2 as fulfilling that certification, but I’m here to dispel any notion that an ISO27k certification is, in any way, evidence that the right controls are in place and operating effectively to protect personal data.
  2. The GDPR requires notification of a data breach to data protection authorities within 72 hours of detection.  As someone who has been involved in far more than my share of investigations, I’ll say that this is not reasonable in most cases.  At least it is not reasonable without providing a lot of half-baked reports, potentially including false positives.  I fear this requirement is going to drive bad behavior… If I’m not actively looking for a breach, I’m much less likely to find that one is happening or has happened.  The GDPR should encourage proactive detection and appropriate investigations.
  3. The GDPR obligates both data controllers (the entity that collects and decides how to process personal data) and data processors (entities engaged by a data controller to perform some processing of personal data) to protect personal data.  This will likely have an adverse effect on IT outsourcing and other IT services because IT suppliers must ensure the data they are entrusted to process is properly protected, regardless of the wishes of their client, the data controller.  This arrangement is inevitably going to lead to significant differences in personal data protection controls between a controller a processor.  Controllers often engage an IT supplier for cost efficiency purposes, meaning that they are unlikely to want to implement a bunch of expensive new controls for something their IT supplier is responsible for performing, while the supplier, being a concentration of regulatory risk across clients, will demand more stringent (and expensive) controls than their client may be willing to tolerate.  I don’t expect that organizations are going to see this as a wake up call to in-source IT, and even if they did, I’m not sure that is good for data subjects in the long run, as IT continues to become more like a utility.  A better model would be to follow the model of nearly every other data protection regulation that exists, where the controller defines the controls required to protect the personal data they process, with penalties for negligence on the part of the supplier, and so on.
  4. The regulation is so obtusely worded that the very people responsible for implementing the mandated protections do not generally understand who it does and does not apply to.  For example, many IT professionals (and indeed EU data subjects at large) seem to believe that the GDPR applies to all organizations anywhere in the world that process the personal data of an EU data subject.  This is not true to the best of my understanding: the GDPR applies to organizations that are present in the EU and process personal data of EU data subjects, and also applies to non-EU organizations that are targeting EU citizens.  This seemingly means that my blog, hosted in Dallas, TX, USA, and which does not specifically target EU data subjects, likely does not need to comply with the GDPR.

What did I miss?  Do you disagree with my observations?


Concentration of Risk and Internally Inconsistent Regulations

Concentration of Risk

Last week, I wrote a post on why we shouldn’t feel bad for cyber insurance carriers because they do a pretty good job of limiting their obligations in the event of a loss.  Since then, I recall reading a story about concentration risk with some insurance providers in Florida and started wondering if there is likely to be a similar challenge evolve in the cyber insurance arena.  The problem in the Florida instance was, as far as I can recall from the story, that a particular carrier insured many houses in Florida.  During a particularly bad year for hurricanes, the carrier went bankrupt because they weren’t able to cover the claims from their many customers.

As I understand it, larger carriers mitigate this risk by maintaining a broad portfolio of insured clients in geographically distributed areas, and limit exposure to any given area through rate adjustments.  Insurance only works when there are many more people paying in than cashing out through claims.  Of course, there are reinsurers who can also help cover losses from a carrier, but they too, have to monitor their risk concentrations.

Hopefully it’s obvious where I’m going here.  In the cyber security world, risk is not concentrated along geographic boundaries, but rather on technical boundaries.  I suspect that the cyber actuaries at insurance providers already have this problem well in hand, but I can see that it will be difficult to avoid risk concentration in cyber insurance, particularly given the mono-cultures we have in technology, that can lead to events such as WannaCry and NotPetya rapidly causing massive damage across geographic and industry lines.  While cyber insurance carriers may be able to limit their exposure on any given policy through liability caps, if all or most of a carrier’s clients simultaneously need to make a claim, we will likely see something similar to the Florida hurricane situation.  From a macro perspective, it seems to me that cyber insurance carriers may be better served to focus more on this technology mono-culture problem, than on monitoring the controls of any given organization.

Internally Inconsistent Regulations

The GDPR has been on my mind a lot lately.  The GDPR mandates that the personal data of EU citizens be both properly protected and accessible.  Exactly what that means is left to the Data Controller and Data Processor to guess, presuming that the data protection authorities will tell them, in the form of a potentially existential fine, if they guess wrong.  The recently disclosed Meltdown and Spectre CPU predictive execution “vulnerabilities” raise an interesting paradox: it may not be possible for an organization to both protect the data AND ensure it is available, at least on certain timelines.  I certainly don’t believe that Meltdown or Spectre create this situation, but they highlight that the situation could exist.

The vendor response to Meltdown and Spectre has been disastrous: vendors are releasing patches, only to later retract them, some software creates incompatibilities with fixes from other vendor, some vendors recommend not installing other vendor fixes, and so on.  And there is no meaningful recourse for technology users beyond waiting for software fixes that potentially cause significant performance impacts and/or random system reboots: hardware vendors have yet to release new processors that fully fix the problem.

If Meltdown and Spectre were more serious vulnerabilities, in terms of the potential risk to personal data, how do Data Controllers and Data Processors balance the risk between the dual mandates of security and availability in the GDPR, with such a potentially significant stick hanging over their heads.  The “regulatory doves”, no doubt, will respond that “regulators would take a measured view in any kind of enforcement situation”. Maybe they would, and maybe they would decide that an organization chose badly when making the determination of whether to shut off it’s systems to protect the personal data, or keep them running (and data vulnerable).

Hopefully this remains in the domain of an academic thought experiment, however with with trend line of EternalBlue, Intel AMT, Intel ME, and now Meltdown/Spectre, it seems highly likely that some organizations will face this decision in the not so distant future.


Probability of Getting Pwnt

I recently finished listening to episode 398 of the Risky Business podcast where Patrick interviews Professor Lawrence Gordon. The discussion is great, as all of Patrick’s shows are, but something caught my attention.  Prof Gordon describes a model he developed many years ago for determining the right level of IT security investment; something that I am acutely interested in.  Professor points out that a key aspect of determining the proper level of investment is the probability of an attack, and he points out that the probability needs to be estimated by the people who know the company in question best: the company’s leadership.

That got me thinking: how do company leaders estimate that probability?  I am sure there are as many ways to do it as there are people doing it, however the discussion reminded me of a key topic in Daniel Kahneman’s book “Thinking Fast and Slow” regarding base rates. Base rates are more or less an average quantity measured against a population for a given concept. For example, the probability of dying in a car crash is about 1 in 470.  That’s the base rate. If I wanted to estimate my likelihood of dying in a car crash, I should start with the base rate and make adjustments I believe are necessary given unique factors to me, such as that I don’t drive to work every day, I don’t drink while driving and so on. So, maybe I end up with my estimate being 1 in 60o. 

If i didn’t use a base rate, how would I estimate my likelihood of dying in a car crash?  Maybe I would do something like this:

Probability of Jerry dying in a car crash <

1/(28 years driving x 365 x 2 driving trips per day) 

This tells me I have driven about 20,000 times without dying. So, I pin my likelihood of dying in a car crash at less than 1 in 20,000. 

But that’s not how it works. The previous 20,000 times I drove don’t have a lot to do with the likelihood of me dying in a car tomorrow, except that I have experience that makes it somewhat less likely I’ll die.  This is why considering base rates are key. If something hasn’t happened to me, or happens really rarely, I’ll assign it a low likelihood. But, if you ask me how likely it is for my house to get robbed right after it got robbed, I am going to overstate the likelihood.

This tells me that things like the Verizon DBIR or the VERIS database are very valuable in helping us define our IT security risk by providing a base rate we can tweak. 

I would love to know if anyone is doing this. I have to believe this is already a common practice. 

Risk Assessments and Availability Bias

This post is part of my continuing exploration into the linkages between behavioral economics and information security.   I am writing these to force some refinement in my thinking, and hopefully to solicit some ideas from those much smarter than I…


In well repeated studies, subjects were asked to estimate the number of homicides in the state of Michigan during the previous year.  The response each subject gave varied primarily based on whether the subject recalled that the city of Detroit is in Michigan.  If Detroit does not come to mind when answering the question, the reported homicide rate is much lower than when Detroit, and it’s crime problem, comes to mind.  This is just one of the many important insights in Daniel Kahneman’s book “Thinking Fast and Slow”.   Kahneman refers to this as the “availability bias”.  I’ve previously written about the availability bias as it relates to information security, but this post takes a bit different perspective.

The implication of the Michigan homicide question on information security should be clear: our assessments of quantities, such as risks, are strongly influenced by our ability to recall important details that would significantly alter our judgement.  This feels obvious and intuitive to discuss in the abstract, however we often do not consider that which we are unaware of.  This is Donald Rumsfeld’s “unknown unknowns”.

In the context of information security, we may be deciding whether to accept a risk based on the potential impacts of an issue.  Or, we may be deciding how to allocate our security budget next year.  If we are unaware that we have the digital equivalent of Detroit residing on our network, we will very likely make a sub-optimal decision.

In practice, I see this most commonly result in problems in the context of accepting risks.  Generally, the upside of accepting a risk is prominent in our minds, and the downsides are obtuse, abstract and we often simply don’t have the details needed to fully understand the likelihood, nor the impact, of the risk that we are accepting.  We don’t know to think about Detroit.

How Do We Know We’re Doing a Good Job in Information Security?

Nearly every other business process in an organization has to demonstrably contribute to the top or bottom lines.

  • What return did our advertising campaign bring in the form of new sales?
  • How much profit did our new product generate?
  • How much have we saved by moving our environment “to the cloud”?

Information security is getting a lot of mind share lately among executives and boards for good and obvious reasons.  However, how are those boards and executives determining if they have the “right” programs in place?

This reminds me of the TSA paradox…  Have freedom gropes, nudie scanners and keeping our liquids in a clear ziplock bag actually kept planes from falling out of the sky?  Or is this just random luck that no determined person or organization has really tried in recent years?

If our organization is breached, or has a less significant security “incident”, it’s clear that there is some room for improvement.  But, do no breaches mean that the organization has the right level of investment, right technologies properly deployed, right amount of staff with appropriate skills and proper processes in place?  Or is it just dumb luck?

Information security is in an even tougher spot than our friends the TSA here.  A plane being hijacked or not is quite deterministic: if it happened, we know about it, or very soon will.  That’s not necessarily the case with information security.   If a board asks “are we secure?”, I might be able to answer “We are managing our risks well, we have our controls aligned with an industry standard, and the blinky boxes are blinking good blinks.”  However, I am blind to the unknown unknowns.  I don’t know that my network has 13 different hacking teams actively siphoning data out of it, some for years.

Back to my question: how do we demonstrate that we are properly managing information security?  This is a question that has weighed on me for some time now.  I expect that this question will grow in importance as IT continues to commoditize and security threats continue to evolve and laws, regulations and fines increase, even if public outrage subsides.  Organizations only have so much money to invest in protection, and those that are able to allocate resources most effectively should be able to minimize costs of both security operations and of business impacts due to breaches.

I recently finished reading “Measuring and Managing Information Risk: A FAIR Approach”, and am currently reading “IT Security Metrics”.  Both are very useful books, and I highly recommend anyone in IT security management read them.   These are generally “frameworks” that help define how, and how not to, assess risk, compare risks and so on.  In the context of a  medium or large organization, using these tools to answer the question “are we doing the right things?” seems intuitive, however at the same time, so mind bogglingly complex as to be out of reach.  I can use these to objectively determine if I am better off investing in more security awareness training or a two factor authentication system, however it won’t inform me that I should have actually spent that extra investment on better network segmentation, since that risk wasn’t on the radar until the lack of it contributed to a significant breach.

Also, there really is no “perfect” security, so we are always living with some amount of risk associated with the investment we make.  Since our organization is only willing or able to invest so much, it explicitly or implicitly accepts some risk.  That risk being realized in the form of a breach does not necessarily mean that our management of information security was improper given the organizational constraints, just as not having a breach doesn’t mean that we ARE properly managing information security.

Without objective metrics that count the number of times we weren’t breached, how does the board know that I am wisely investing money to protect the organization’s data?

Is this a common question?  Are good leaders effectively (and responsibly) able to answer the question now?  If so, how?



Certainty, Cybersecurity and an Attribution Bonus

In “Thinking Fast And Slow”, Daniel Kahneman describes a spectrum of human irrationalities, most of which appear to have significant implications for the world of information security.  Of particular note, and the focus of this post, is the discussion on uncertainty.

Kahenman describes that people will generally seek out others who claim certainty, even when there is no real basis for expecting someone to be certain.  Take the example of a person who is chronically ill.  A doctor who says she does not know the cause of the ailment will generally be replaced by a doctor who exhibits certainty about the cause.  Other studies have shown that the doctor who is uncertain is often correct, and the certain doctor is often incorrect, leading to unnecessary treatments, worry, and so on.  Another example Kahneman cites is the CFO of companies.  CFO’s are revered for their financial insight, however they are, on average, far too certain about things like the near term performance of the stock market.  Kahneman also points out that, just as with doctors, CFOs are expected to be certain and decisive, and not being certain will likely cause both doctors and CFOs to be replaced.  All the while the topic each is certain about is really a random process, or such a complicated process containing so many unknown and unseen influencing variables as to be indistinguishable from randomness.

Most of us would be rightly skeptical about someone who claims to have insight into the winning numbers of an upcoming lottery drawing, and would have little sympathy when that person turns out to be wrong.  However, doctors and CFOs have myriad opportunity to highlight important influencing variables that weren’t known when their prediction was made.  These variables are what make the outcome of the process random in the first place.

The same dichotomy regarding irrational uncertainty of random processes appears to be at work in information security as well.  Two examples are the CIO who claims that an organization is secure, or at least would be secure if she had an additional $2M to spend, and the forensic company that attributes an attack on a particular actor – often a country.

The CIO, or CISO, is in a particularly tough spot.  Organizations spend a lot of money on security and want to know whether or not the company remains at risk.  A prudent CIO/CISO will, of course, claim that such assurances are hard to give, and yet that is the mission assigned to them by most boards or management teams.  They will eventually be expected to provide that assurance, or else a new CIO/CISO will do it instead.

The topic of attribution, though, seems particularly interesting.  Game theory seems to have a strong influence here.  The management of the breached entity wants to know who is responsible, and indeed the more sophisticated the adversary appears to be, the better the story is.  No hacked company would prefer to report that their systems were compromised by a bored 17 year old teaching himself to use Metasploit over the adversary being a sophisticated, state-sponsored hacking team the likes of which are hard, neigh impossible for an ordinary company to defend against.

The actors themselves are an intelligent adversary, generally wanting to shroud their activities with some level of uncertainty.  We shouldn’t expect that an adversary will not  mimic other adversaries, reuse code, fake timezones, change character sets, incorporate cultural references, and so on, of other adversaries in an attempt to deceive.  These kinds of things add only marginal additional time investment to a competent adversary.  As well, other attributes of an attack, like common IP address ranges, common domain registrars and so on, may be common between adversaries for reasons other than the same actor is responsible, such as that of convenience or, again, an intentional attempt to deceive.  Game theory is at play here too.

But, we are certain that the attack was perpetrated by China.  Or Russia. Or Iran. Or North Korea. Or Israel.  We discount the possibility that the adversary intended for the attack to appear as it did.  And we will seek out organizations that can give us that certainty.  A forensic company that claims the indicators found in an attack are untrustworthy and can’t be relied upon for attribution will most likely not have many return customers or referrals.

Many of us in the security industry mock the attribution issue with dice, an magic 8-ball and so on, but the reality is that it’s pervasive for a reason: it’s expected, even if it’s wrong.