Developers have the objective of building a functional application. They are focused on building more functionality into applications. Moreover, building security creates more workload  for Developers which is a disincentive and moreover, Developers are rewarded for building more functionality than building more security. I have never seen a Developer in my professional life for being rewarded for building a secure application.

Hackers are focused on how to break the application. They look for weak links in application that will enable them to access application data. Developers usually follow process to build application, but Hackers have no process and all they have is multitude of possibilities. Hackers are innovative in trying various permutations in compromising the application.

A million dollar question is whether we can build secure applications when a Developer is focused on functionality but not on breaking the application?

There is a school of thought about Inside-out security where the application is built securely from scratch. Unfortunately,  this approach won't suffice because hackers traverse Outside-in. A little reflection will highlight the importance of vulnerability scanning and penetration testing of application. This will bring the perspective of what developers do not know already.

Building a secure application inside out is not enough. In order to address unknown unknowns (or blind spots of developers), penetration testing should be done. Both whitebox style penetration testing (where components of an application is known)  and also blackbox style penetration testing which mi micks an Hacker who may not have any knowledge of the application, should be carried out.

An application of higher level of security is not built just by Developers. It is built by integrative process of Developer mindset and Hacker mindset.  This is a constant struggle for years to come.
 

When a data owner's (a business) sensitive data is breached it is difficult to quantify the monetary loss. According to respectable survey sources, the average cost of sensitive data breach for a large size company is about $50,000. I am attempting here to think about this in simple mathametical terms:

There is a data breach. From the data owner's perspective the loss is:

Loss = Cost to protect data + Loss of business due to data theft aka cost of competitive disadvantage

From the data thief's perspective

Net Gain= [Cost of producing the data  *  Data freshness factor] - Cost to steal the data + Profit of business due to data aka gain of competitive advantage

From the above two equations it is very clear that this is not a zero sum game. There is a clear cost asymmetry for a data owner and for a data thief. When there is an asymmetry there is an opportunity. Data owner would not even know that the data is lost because the original copy of the data may be still intact - data thief could have simply copied the data. Data theft does not look like a car theft, there is no vacuum left behind. 

This motivates a data thief to keep the cost to steal low, steal highly valuable data that has a long shelf life and in a way that data owner will never even be aware of theft.

From a data thief's perspective, the cost to steal data if kept high would disincentive him. Moreover, Data freshness factor, i.e. how valuable this data is over period of time plays an important role. A good example is content of today's newspaper is hardly valuable tomorrow, but the content of newspaper two days ahead (if can be procured)would be invaluable. Data relevance is a function of time and other marketplace variables -  Data freshness Factor accounts for that variable. A good way to discourage data thief is to increase his/her cost to steal the data. There are other inferences from the above equation. If there exists no competitive advantage with the stolen data, hardly any thief would even venture to steal the data in the first place. If the cost of producing data is very low, then probably thief can just produce the data himself and would not attempt to steal the data. If the cost of theft is kept high, it would definitely deter the data thief from stealing data using technical mechanisms, then the data thief would exploit weak links in data security such as use of social engineering to get access to the data.

From data owner perspective protecting data becomes very important. How much would the owner be willing to spend? Not definitely the cost equal to cost of producing the data. 1% to 10% of cost of producing data is considered prudent. For a data owner it is difficult to estimate cost of data protection of a specific data, because it is not easy to chunkify data protection costs. Moreover, as Dan Geer says in his book, a data owner has to protect himself from number of intruders not just one.

It pays for a data owner to: be aware of data breaches (or data leaks), employ appropriate mechanisms to protect the data; the cost of protection which is fractional cost of the valuable data and enhance information security awareness of personnel who handle the data.

Data loss is not a zero sum game. The advantage is in favor of a data thief (data thieves rather). Data owner does not give much thought on the value of data unless there is a data theft. But, a data thief has every reason to think about economics of data theft before he acts to steal the data else data thief won't survive in this game and he is very well aware of his advantageous position.

On the CD-ROM accompanying the book we included a tool called Passgen. In the book, we recommended that you maintain separate passwords on every local administrator and service account in your enterprise. This is, of course, almost impossible to manage without something to automate it for you. That’s what Passgen does. The tool generates unique passwords based on known input (an identifier and passphrase you define), sets those passwords remotely, and allows you to retrieve them later.

For a while Jesper maintained a web site for the book, running on a server in his house. His ISP changed policies and made it impractical to continue running the site. But because the tool is still so useful, I’ve put a copy in my SkyDrive—look in the “Passgen” folder.

Also, note that I’ve put a new section in the right-side column, “Resources for you.” Here’s where I’ll keep links to bits and pieces that many of you will find relevant and interesting.

So, in conjunction with the BlueHat team, I am pleased to announce that the SDL team will be organizing the sessions for the second day of the fall BlueHat conference. The BlueHat SDL sessions will be laser-focused on not just describing vulnerabilities but also solving them. Every attendee should leave every presentation with a clear idea of exactly what he or she needs to do to protect themselves from the threat that was discussed during the session.

The sessions will begin, appropriately, with the topic of secure design. Danny Dhillon of EMC and the SDL team’s own Adam Shostack will each present their organization’s approach to threat modeling. As a bonus, Adam will also be demonstrating the new SDL Threat Modeling tool that you might have heard about last week.

Next up is Matt Miller, a recent and very welcome addition to the Microsoft Security Science team. Matt has a fantastic presentation on the evolution of buffer overflow attacks and on the corresponding development of overflow mitigations. From there we will switch gears to look at some managed code implementation issues: iSEC Partners’ Scott Stender and Alex Vidergar will demonstrate coding techniques to mitigate elusive concurrency vulnerabilities in web applications.

At this point we will have covered the Design and Implementation phases of the SDL; where better to go from here than Verification? One of the most important activities in the Verification phase is fuzzing, and we have a trio of security experts from the Microsoft Security Science team to talk about it. Jason Shirk, Lars Opstad, and Dave Weinstein will answer three of the most common fuzzing questions: How should I fuzz? When have I fuzzed enough? And what do I do now that I’ve fuzzed?

Finally, we will wrap up the Verification phase talks with a return appearance to BlueHat by Stach & Liu’s Vinnie Liu. Vinnie will compare different approaches to security verification – static code analysis, blackbox analysis, and manual code review – and make recommendations as to when each approach is best used.

Even if you can’t make it in to BlueHat in person, you can still watch the sessions via streaming media on TechNet. Additionally, webcast interviews with the speakers – condensed “Cliff’s Notes” versions of their full presentations – will be posted on Channel 9. And we’ll be continuing the BlueHat tradition of inviting speakers and other industry notables to guest blog about their topics and the latest security trends. More information on all of these resources will be posted here when it becomes available.

For example, the “Average Joe Investor” does not care about “best order execution” or “smart order routing,” this is for “the big boys.”  As we all know, saving a few pennies or dollars per transaction to “Average Joe Investor” does nothing for them when their retirement nest egg is lost due to corporate greed and negligence.     The folks who “really care” about shaving a few milliseconds off market execution are the companies that are trading high volumes of exotic derivatives and baskets who have, for the most part, zero interest in the personal financial portfolio of “Jane in Iowa” or “Joe in Kansas.”

I am really amazed to see the dominance of greed in corporate America and the lack of corporate social responsibility.  Risk taking and “split second trading” does little for any small. individual investor and has proven to destabilize our society.    Who cares about saving a few pennies or dollars in market executive?

The answer: Only the greedy corporations, the same people responsible for the current destabilization, chao and near collaspe of our entire financial system.   Homes lost, unprecedented bankruptcies. and money market funds less than par value!   You no doubt have read that folks in the Reserve Money Market funds cannot even withdraw their “safe money.”  Investors in the Reserve Funds are being told that for every dollar they invested in a money market, they now only have 97 cents and cannot withdraw their capital as the Reserve waits for a government bailout.

What is to blame? Greed and profits over corporate social responsibility are to blame.

I read where some folks think the government needs to regulate market-related news, supposedly to stabilize trading based on news.   Regulating news has another name -  “censorship” - but who cares about the US Constitution when money and split second algo trading is involved?    I am amazed.   Folks in financial services just will say or do anything to make a buck, or keep from losing one, even at the expense of society and our basic constitutional freedoms.  News is not regulated in our democratic society, nor should it be to make algorithmic trading “better”.     What we need is less split second, computerized algo trading and more stablity.   Machine processing should not dicate nor mandate changes to our democratic principles.

Nor should our lives in a free society be censored or regulated because of the trading requirements for split second transactions that benefit large corporations.    The average investor does not need an unstable financial system trading exotic derivatives and baskets at the speed of light.  This requirement is driven by corporate greed that destabilizes the core economy and fabric of our society.

Of couse, many of the same folks would like for us to believe that technology is the answer.  This is a fallacy.

Corporate greed is destabilizing society.   What need to be regulated is not the news, but corporate risk taking and corporate goverance.  Individual investors do not need lightspeed transactions in an unstable world.   Citizens and families need a secure, stable economic infrastructure, something that has been lost in the culture of corporate greed, but hopefully not forever.

I’d love to have another blog even more specific - “Ok, that Assurance is Evidence Of What, Exactly?

Today he has a great article called:

What’s the matter with Risk Management?

And “in short, it’s everything.” It pretty much sums up why I had to grow to re-evaluate how our industry does risk, risk management, approaches controls & vulnerability and find a new way.   A couple of things jump out at me in reading Chris’ article:

1.)  Just because that Deming cycle sucks and is full of unknowns doesn’t mean “risk” doesn’t exist, nor that it isn’t of primary importance. Nor does it mean that in the absence of model & methodology, we won’t be “doing” risk analysis anyway - just in an ad hoc method and completely from “the gut”.

Our industry calls these unstructured risk analysis “Best Practices”, as it’s an easy and convenient way of sweeping the unknowns under the rug of bureaucracy and enforcing it via peer pressure.

2.)  What this “suckiness” does mean is that your model and methodology aren’t helping you. As Chris intimates, there is too much uncertainty in the inputs for his model (they are, in the language of Bayesians - too subjective to be useful priors).

Take for example how we might be approaching the “controls” part of our analysis.  Chris writes:

“2. What are the controls that we have to employ?
800-53, ISO 27001, PCI, etc.

Still kinda good, but we basically know that ISO is relatively voluntary and NIST supplies a control catalog and not policies. So here we have to take the control catalog, and mash our policies into it.”

I wouldn’t call this “kinda good” at all :)  These control catalogs only provide a hierarchy within which to look for evidence of  our ability to resist an attacker.  They are incapable of making any claim about the effectiveness of the controls when they are operated at 100% efficiency, or more importantly, what % efficiency our specific organization operates at.

Let’s use Chris Hayes’ Initech as our fictional example.

Initech has a control (a back door on a loading dock).  Now the locks on the door are 100% capable of locking the door.  This is different than saying that they are capable of frustrating all but the top 5% of lockpicking burgalars.  It is also diffferent than saying that in a sample of several “walk around audits” the doors are left open 20% of the time (they are not in compliance with policy 100% of the time).  Even worse, that 80% of the time the door is not propped open?  Yeah, tailgating is a known issue.

So we have several different variables here that we need to account for (and it’s just a door).  But the analogy stands that most “risk management” methodologies are “We have a door, yes/no?” And most GRC platforms, when asked for their “opinion” will simply say “door is needed” or, even worse, “a door policy is needed”.

3.)  Criticality and the Source of Value is all messed up in these Risk Management models.

Chris writes:

Someone wants me to tell them which boxes are more critical than others. This is mainly because of budgetary or operational reasons. To which I usually say “All of them, it is a system after all”.

This literally made me laugh out loud.  And this sort of “rate the firewall as Risk = 500 but rate the actual business application as Risk = 157″ thing is also endemic.  Now Chris is very smart here.  He correctly identifies that the value is tied to the business process the systems support, and not to a specific box.  Oh, we scan at the specific box level - but because of the nature of systemic failures - all the boxes in the process are inexorably interrelated.

One of the reasons I really like FAIR is that the losses are quantified (or qualified) based not on some amorphous value of the box or the process itself, but losses are linked to the actions that the threat will take. Take systems in a highly regulated industries as an example.  Usually the most probable losses aren’t due to system compromise per se, but in the disclosure the compromise causes (regulators are a threat source, after all).  But many “risk management” methodologies will say “online banking is worth $2 billion, the value of the systems is therefore $2 billion”.  And suddenly we’re telling executive management that there’s a 60% probability that they’ll lose $2 billion.

4.)  If the primary source of prior information for your “risk management” methodology is a vulnerability scanner - you’re doing it wrong.  Chris writes:

So we ran a scan and now we have a report. A snapshot in time to make all decisions. Where did these vulnerability ratings come from? Do I even know if my system is at risk? What if I spend my time on vulnerabilities that have no threat?

So first, my thoughts are that actual “vulnerability” must be a comparison of the force a threat can apply, and our ability to resist that force (this is a probability statement, btw).

Changing your thinking about vulnerability now helps us understand the problem in several new ways.  First, you can start to divorce yourself from the scanner.  After all, the scanner is simply providing you with current state information that is usually just relevant variance from policy. It doesn’t really tell you about real “weakness in a system” because the system is an interrelated mess of people, processes and IT assets.

5.)  Finally, most “risk management” approaches just *don’t* do a good job of helping us understand the how’s and why’s of managing risk. In the past, I’ve referred to these standards as really being “issue management” because they are at their heart, an act of discovery - a formal process around gathering prior information.  They are not, in and of themselves, capable of linking the issues discovered to the root cause.  And these root causes?  Yeah, they’re the things that create “risk”.  Not a threat, not a vulnerability, not the existence of an asset - the amount of risk that we have stems from our capability to manage it.

So Chris, I completely agree - but I wouldn’t give up yet.  There actually are a few of us who are focused on what you suggest:

Where to go from here: A fundamental revamp of how to deal with Risk. Where risk professionals focus on the treating the sickness and not the symptoms, and come up with some new success/actionable metrics.

Chris, there’s nothing I want to do more than that.