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We get a lot of requests to sponsor this blog. We got several this week. Not just the spammy “Please link with us,” or “Host our content and make BIG $$$” stuff. And not the PR junk that says “We are absolutely positive your readers would just love to hear what XYZ product manager thinks about data breaches,” or “We just released 7.2.2.4 version of our product, where we changed the order of the tabs in our web interface!” Yeah, we get fascinating stuff like that too. Daily. But that’s not what I am talking about. I am talking about really nice, personalized notes from vendors and others interested in supporting the Securosis site. They like what we do, they like that we are trying to shake things up a bit, and they like the fact that we are honest in our opinions. So they write really nice notes, and they ask if they can give us money to support what we do. To which we rather brusquely say, “No”. We don’t actually enjoy doing that. In fact, that would be easy money, and we like as much easy money as we can get. More easy money is always better than less. But we do not accept either advertising on the site or sponsorship because, frankly, we can’t. We just cannot have the freedom to do what we do, or promote security in the way we think best, if we accept payments from vendors for the blog. It’s like the classic trade-off in running your own business: sacrifice of security for the freedom to do things your own way. We don’t say “No,” to satisfy some sadistic desire on our part to be harsh. We do it because we want the independence to write what we want, the way we want. Security is such a freakin’ red-headed stepchild that we have to push pretty hard to get companies, vendors, and end users to do the right thing. We are sometimes quite emphatic to knock someone off the rhythm of that PowerPoint presentation they have delivered a hundred times, somehow without ever critically examining its content or message. If we don’t they will keep yakking on and on about how they address “Advanced Persistant Threats.” Sometimes we spotlight the lack of critical reasoning on a customer’s part to expose the fact that they are driven by politics without a real plan for securing their environment. We do accept sponsorship of events and white papers, but only after the content has gone through community review and everyone has had a chance to contribute. Many vendors and a handful of end-users who talk with us on the phone know we can be pretty harsh at times, and they still ask if they economically support our research. And we still say, “No”. But we appreciate the interest, and we thank you all for for participating in our work. On to the Summary: Webcasts, Podcasts, Outside Writing, and Conferences Adrian’s Dark Reading article on What Oracle Gets In The Secerno Buy. Rich quoted in a Dark Reading article on database passwords. Did we mention Rich was on NPR Science Friday? The full transcript is up. Unfortunately – since it has all the “you knows” and “ums” in it. Adrian’s DAM Deployment Issues to Avoid launched this week. Rich on the Network Security Podcast. Adrian quoted in CRN Tech on database security. Mike quoted in SC Magazine. Favorite Securosis Posts Rich: Code Re-engineering. This applies to so much more than code. I’ve been on everything from mountain rescues to woodworking projects where the hardest decision is to stop patching and nuke it from orbit. We are not mentally comfortable throwing away hours, days, or years of work; and the ability to step back, analyze, and start over is rare in any society. Mike Rothman: Code Re-engineering. Adrian shows his development kung fu. He should get pissed off more often. David Mortman: Gaming the Tetragon. Adrian Lane: The Secerno Technology. Just because you need to understand what this is now that Oracle has their hands on it. Other Securosis Posts Understanding and Selecting SIEM/LM: Aggregation, Normalization, and Enrichment. Quick Wins with DLP Presentation. Incite 5/26/2010: Funeral for a Friend. Understanding and Selecting SIEM/LM: Data Collection. A Phish Called Tabby. Thoughts on Diversity and False Diversity. FireStarter: The Only Value/Loss Metric That Matters. The Laziest Phisher in the World. Favorite Outside Posts Rich: Data Loss Prevention and Enterprise Rights Management; Complimentary or alternative? For 6 months or so I’ve been getting a lot of “which is better, DRM or DLP?” questions. The problem is that they are not alternative technologies, but complementary. The trick is to figure out which one might be more appropriate to implement first, not which can replace the other. Besides, I think they are on the path to complete convergence in the long term, and we already have early samples of combined solutions. Adrian: Bejtlich’s Forget Pre-Incident Cost, How Much Did Your Last Incident Cost? Almost picked Rich’s post The Only Value/Loss Metric That Matters for my internal fave of the week, but this is like a two-fer. Mike Rothman: Google Secure Search and Security Overkill. Boaz makes the point that not all security is worth it. Playing at a security theater near you…. David Mortman: Privacy Theater. Project Quant Posts DB Quant: Discovery And Assessment Metrics (Part 2) Identify Apps. DB Quant: Discovery And Assessment Metrics (Part 1) Enumerate Databases. DB Quant: Planning Metrics (Part 4). Research Reports and Presentations Understanding and Selecting a Database Encryption or Tokenization Solution. Low Hanging Fruit: Quick Wins with Data Loss Prevention. Report: Database Assessment. Top News and Posts TabNabbing was the big news this week. Three indicted on $100M Rogue Software Scam. Mozilla Plugin Check via Brian Krebs. Supposed Vuln in iPhone Encryption. Oopsie. Why does the IRS never have a problem like this? Your Privacy in Their Hands via LiquidMatrix. Can you have a PCI Compliant Virtual Site? Good question. New School blog announces The

When I was abroad on vacation recently, the conversation got to the relative cost of petrol (yes, gasoline) in the States versus pretty much everywhere else. For those of you who haven’t travelled much, fuel tends to be 70-80% more expensive elsewhere. Why is that? It comes down to the fact that the US Government bears many of real costs of providing a sufficient stream of petroleum. Those look like military, diplomatic, and other types of spending in the Middle East to keep the oil flowing. I’m not going to descend into either politics or energy dynamics here, but suffice it to say we’d be investing a crapload more money in alternative energy if US consumers had to directly bear the full brunt of what it costs to pull oil out of the Middle East. With that thought in the back of my mind, I checked out one of Bejtlich’s posts last weekend which talked about the R&D costs of the bad guys. Basically these folks run businesses like anyone else. They have to invest in their ‘product’, which is finding new vulnerabilities and exploiting them. They also have to invest in “customer service,” which is basically staying invisible once they are inside to avoid detection. And these costs are significant, but compared to the magnitude of the ‘revenue’ side of their equation, I’m sure they are happy to make the investment. Cyber-fraud is big business. But what about other hidden costs of providing security? We had a great discussion on Monday with the FireStarter talking about value/loss metrics, but do these risk models take into account some of the costs we don’t necessarily see as part of security? Like our network traffic. How much bandwidth is wasted on reconnaissance traffic looking for holes in our perimeters? What about the amount of your inbound pipe congested with spam, which you need to analyze and then drop. One of the key reasons anti-spam services took off is because the bandwidth demand of spam was transferred to the service provider. What would we do differently if we had to allocate those hidden costs to the security team? I know, at the end of the day it’s all just overhead, but what if? Would it change our behavior or our security architectures? I suspect we’d focus much more on providing clean pipes and having more of our security done in the cloud, removing some of these hidden costs from our IT stack. That makes economic sense, and we all know most of what we do ultimately is driven by economics. How about the costs of cleaning up an incident? Yes, there are some security costs in there from the standpoint of investigation and forensics, but depending on the nature of the attack there will be legal and HR resources required, which usually don’t make it into the incident post-mortem. Or what about the opportunity cost of 1,000 folks losing their authentication tokens and being locked out of the network? Or the time it takes a knowledge worker to jump through hoops to get around aggressive web filtering rules? Or the cost of false positives on the IPS that block legitimate business traffic and break critical applications? We know how big the security budget is, but we don’t have a firm grasp of what security really costs our businesses. If we did, what would we do differently? I don’t necessarily have an answer, but it’s an interesting question. As we head into Memorial Day weekend here in the US, we need to remember obviously, all the soldiers who give all. But we also need to remember the ripple effect of every action and reaction to the bad guys. Every time I go through a TSA checkpoint in an airport, I’m painfully aware of the billions spent each month around the world to protect air travel, regardless of whether terrorists will ever attack air travel again. I guess the same analogy can be used with security. Regardless of whether you’re actually being attacked, the costs of being secure add up. Score another one for the bad guys. Share:

In the last post on Data Collection we introduced the complicated process of gathering data. Now we need to understand how to put it into a manageable form for analysis, reporting, and long-term storage for forensics. Aggregation SIEM platforms collect data from thousands of different sources because these events provide the data we need to analyze the health and security of our environment. In order to get a broad end-to-end view, we need to consolidate what we collect onto a single platform. Aggregation is the process of moving data and log files from disparate sources into a common repository. Collected data is placed into a homogenous data store – typically purpose-built flat file repositories or relational databases – where analysis, reporting, and forensics occur; and archival policies are applied. The process of aggregation – compiling these dissimilar event feeds into a common repository – is fundamental to Log Management and most SIEM platforms. Data aggregation can be performed by sending data directly into the SIEM/LM platform (which may be deployed in multiple tiers), or an intermediary host can collect log data from the source and periodically move it into the SIEM system. Aggregation is critical because we need to manage data in a consistent fashion: security, retention, and archive policies must be systematically applied. Perhaps most importantly, having all the data on a common platform allows for event correlation and data analysis, which are key to addressing the use cases we have described. There are some downsides to aggregating data onto a common platform. The first is scale: analysis becomes exponentially harder as the data set grows. Centralized collection means huge data stores, greatly increasing the computational burden on the SIEM/LM platform. Technical architectures can help scale, but ultimately these systems require significant horsepower to handle an enterprise’s data. Systems that utilize central filtering and retention policies require all data to be moved and stored – typically multiple times – increasing the burden on the network. Some systems scale using distributed processing, where filtering and analysis occur outside the central repository, typically at the distributed data collection point. This reduces the compute burden on the central server and allows processing to occur on smaller, more manageable data sets. It does require that policies, along with the code to process them, be distributed and kept current throughout the network. Distributed agent processes are a handy way to “divide and conquer”, but increase IT administration requirements. This strategy also adds a computational burden o the data collection points, degrading their performance and potentially slowing enough to drop incoming data. Data Normalization If the process of aggregation is to merge dissimilar event feeds into one common platform, normalization takes it one step further by reducing the records to just common event attributes. As we mentioned in the data collection post, most data sources collect exactly the same base event attributes: time, user, operation, network address, and so on. Facilities like syslog not only group the common attributes, but provide means to collect supplementary information that does not fit the basic template. Normalization is where known data attributes are fed into a generic template, and anything that doesn’t fit is simply omitted from the normalized event log. After all, to analyze we want to compare apple to apples, so we throw away an oranges for the sake of simplicity. Depending upon the SIEM or Log Management vendor, the original non-normalized records may be kept in a separate repository for forensics purposes prior to later archival or deletion, or they may simply be discarded. In practice, discarding original data is a bad idea, since the full records are required for any kind of legal enforcement. Thus, most products keep the raw event logs for a user-specified period prior to archival. In some cases, the SIEM platform keeps a link to the original event in the normalized event log which provides ‘drill-down’ capability to easily reference extra information collected from the device. Normalization allows for predicable and consistent storage for all records, and indexes these records for fast searching and sorting, which is key when battling the clock in investigating an incident. Additionally, normalization allows for basic and consistent reporting and analysis to be performed on every event regardless of the data source. When the attributes are consistent, event correlation and analysis – which we will discuss in our next post – are far easier. Technically normalization is no longer a requirement on current platforms. Normalization was a necessity in the early days of SIEM, when storage and compute power were expensive commodities, and SIEM platforms used relational database management systems for back-end data management. Advances in indexing and searching unstructured data repositories now make it feasible to store full source data, retaining original data, and eliminating normalization overhead. Enriching the Future In reality, we are seeing a number of platforms doing data enrichment, adding supplemental information (like geo-location, transaction numbers, application data, etc.) to logs and events to enhance analysis and reporting. Enabled by cheap storage and Moore’s Law, and driven by ever-increasing demand to collect more information to support security and compliance efforts, we expect more platforms to increase enrichment. Data enrichment requires a highly scalable technical architecture, purpose-built for multi-factor analysis and scale, making tomorrow’s SIEM/LM platforms look very similar to current business intelligence platforms. But that just scratches the surface in terms of enrichment, because data from the analysis can also be added to the records. Examples include identity matching across multiple services or devices, behavioral detection, transaction IDs, and even rudimentary content analysis. It is somewhat like having the system take notes and extrapolate additional meaning from the raw data, making the original record more complete and useful. This is a new concept for SIEM, so the enrichment will ultimately encompass is anyone’s guess. But as the core functions of SIEM have standardized, we expect vendors to introduce new ways to derive additional value from the sea of data they collect. Other Posts in Understanding and Selecting SIEM/LM Introduction. Use Cases,

I don’t like to think of myself as a sentimental guy. I have very few possessions that I really care about, and I don’t really fall into the nostalgia trap. But I was shaken this week by the demise of a close friend. We were estranged for a while, but about a year ago we got back in touch and now that’s gone. I know it’s surprising, but I’m talking about my baseball glove, a Wilson A28XX, vintage mid-1980’s. You see, I got this glove from my Dad when I entered little league, some 30+ years ago. It was as big as most of my torso when I got it. The fat left-handed kid always played first base, so I had a kick-ass first baseman’s glove and it served me well. I stopped playing in middle school (something about being too slow as the bases extended to 90 feet), played a bit of intramural in college, and was on a few teams at work through the years. A few of my buddies here in ATL are pretty serious softball players. They play in a couple leagues and seem to like it. So last year I started playing for my temple’s team in the Sunday morning league with lots of other old Jews. I dug my glove out of the trunk, and amazingly enough it was still very workable. It was broken in perfectly and fit my hand like a glove (pun intended). It was like a magnet – if the ball was within reach, that glove swallowed it and didn’t give it up. But the glove was showing signs of age. I had replaced the laces in the webbing a few times over the years, and the edges of the leather were starting to fray. Over this weekend the glove had a “leather stroke”, when the webbing fell apart. I could have patched it up a bit and probably made it through the summer season, but I knew the glove was living on borrowed time. So I made the tough call to put it down. Well, not exactly down, since the leather is already dead, but I went out and got a new glove. Like with a trophy wife, my new glove is very pretty. A black leather Mizuno. No scratches. No imperfections. It even has a sort-of new-car smell. I’ll be breaking it in all week and hopefully it’ll be ready for practice this weekend. For an anti-nostalgia guy, this was actually hard, and it will be weird taking the field with a new rig. I’m sure I’ll adjust, but I won’t forget. – Mike Photo credits: “Leather and Lace” originally uploaded by gfpeck Incite 4 U I want to personally thank Rich and the rest of the security bloggers for really kicking it into gear over the past week. Where my feed reader had been barren of substantial conversations and debate for (what seemed like) months, this week I saw way too much to highlight in the Incite. Let’s keep the momentum going. – Mike. Focus on the problem, not the category – Stepping back from my marketing role has given me the ability to see how ridiculous most of security marketing is. And how we expect the vendors to lead us practitioners out of the woods, and blame then when they find another shiny object to chase. I’m referring to NAC (network access control), and was a bit chagrined by Joel Snyder’s and Shimmy’s attempts to point the finger at Cisco for single-handedly killing the NAC business. It’s a load of crap. To be clear, NAC struggled because it didn’t provide must-have capabilities for customers. Pure and simple. Now clearly Cisco did drive the hype curve for NAC, but amazingly enough end users don’t buy hype. They spend money to solve problems. It’s a cop-out to say that smaller vendors and VCs lost because Cisco didn’t deliver on the promise of NAC. If the technology solved a big enough problem, customers would have found these smaller vendors and Cisco would have had to respond with updated technology. – MR I can haz your ERP crypto – Christopher Kois noted on his blog that he had ‘broken’ the encryption on the Microsoft Dynamics GP, the accounting package in the Dynamics suite from the Great Plains acquisition. Encrypting data fields in the database, he noticed odd behavioral changes when altering encrypted data. What he witnessed was that if he changed a single character, only two bytes of encrypted data changed. With most block ciphers, if you change a single character in the plaintext, you get radically different output. Through trial and error he figured out the encryption used was a simple substitution cipher – and without too much trouble Kois was able to map the substitution keys. While Microsoft Dynamics does run on MS SQL Server, there are some components that still rely upon Pervasive SQL. Christopher’s discovery does not mean that MS SQL Server is secretly using the ancient Caesar Cipher, but rather that some remaining portion Great Plains does. It does raise some interesting questions: how do you verify sensitive data has been removed from Pervasive? If the data remains in Pervasive, even under a weak cipher, will your data discovery tools find it? Does your discovery tool even recognize Pervasive SQL? – AL Blame the addicts – When I was working at Gartner, nothing annoyed me more than those client calls where all they wanted me to do was read them the Magic Quadrant and confirm that yes, that vendor really is in the upper right corner. I could literally hear them checking their “talked to the analyst” box. An essential part of the due diligence process was making sure their vendor was a Leader, even if it was far from the best option for them. I guess no one gets fired for picking the upper right. Rocky DeStefano nails how people see the Magic Quadrant in his Tetragon of Prestidigitation post. Don’t blame the analyst

I just ran across a really interesting blog post by Joel Spolsky from last April: Things You Should Never Do, Part 1. Actually. the post pissed me off. This is one of those hot-button topics that I have had to deal with several times in my career, and have had to manage in the face of entrenched beliefs. His statement is t hat you should never rewrite a code base from scratch. The reasoning is “No major firm has ever successfully survived a product rewrite. Just look at Netscape … ” Whatever. I am a fixer. I was the guy who was able to make code reliable. I was the guy who found and fixed the obscure bugs. As I progressed in my career and started to manage teams of developers, more often than not I was handed the really crummy re-engineering projects because I could fix the problems and make customers happy. Sometimes success is its own penalty. I have inherited code so bad that bug fixes cost 4x in time and usually created new bugs in the process. I have inherited huge bodies of Java code written entirely as if Java were a 3G procedural language – ignoring the object-oriented paradigm completely. I have been tasked with fixing code that – for a simple true/false comparison – made 12 comparisons, 8 database, insertions and 7 deletions – causing an 180x performance penalty. I have inherited code so bad it broke the compiler. I have inherited code so bad that you could not change a back-end database query without breaking the GUI! It takes a real gift for bad programming to do these things. There are times when the existing code – all or part – simply needs to be thrown away. There are times that code is so tightly intertwined that you cannot simply fix one piece at a time. And in some cases there are really good business reasons, like your major customers say your code is crap and needs to be thrown away. Bad code can bleed a company to death with lost sales, brand impairment, demoralization, and employee turnover. That said, I agree with Joel’s basic premise that re-writing your product can kill your company. And I even agree about a lot of the social behaviors he describes that create failure. There is absolutely no reason to believe that the people who developed bad code the first time will not do the same thing the next time. But I don’t agree that you should never rewrite. I don’t agree that it has never been done successfully. I know because I have done it successfully. Twice. Out of three attempts, but hey, I got the important projects right. We tend not to hear about successful rewrites because the companies that carried it off really don’t want everyone knowing that previous versions were terrible. They would rather focus on happy customers and competitive products. It’s very likely that companies who need to rewrite code will screw up a second time. Honestly, there are a lot more historic rewrite flameouts than success stories. Companies know what they want to fix in the code, but they don’t understand what they need to fix in the company. I contend this is because there are company behaviors that promote failure, and if they did it once, they are likely to do it again. And again. Until, mercifully, the company goes down in flames. There are a lot of reasons why re-architecture and re-implementations projects fail. In no particular order … Big eyes: You are the chief developer and you hate your current product. You have catalogued everything that is wrong with it and how you would fix it. You have extensive lists of features you would like to implement. You have a grand vision of how this product should function, how it should be architected, and how it will be implemented. This causes your re-engineering effort to fail because you think that you are going to build perfect software, tackle every problem, and build every feature, in the first revision. And you commit to do so, just to get the project green-lighted. Resources: You current product sucks. It really sucks. It has atrocious quality and low performance, and is miserable to manage. It’s so freaking bad that customers ask for their money back, and sales falter. This causes your re-engineering effort to fail because there is simply not enough time, and not enough revenue to pay for your rebuild. Not with customers breathing down management’s neck, and investors looking for the quick “liquidity event”. So marketing keeps on marketing, sales keeps on selling, and you keep on supporting the old mess you have. Bad blood: When you car gets old and dies, you don’t expect someone to give you a new one for free. When your crappy old code no longer supports your customers, in essence you need to pay for new code. Yes, it is unfortunate that you bought a lemon last time, but you need to make additional investments in time and development resources, and fix the problems that led you down the wrong path. Your project fails because management is so bitter about the failure that they muck around with development practices, apply more pressure and try to get more involved with day-to-day development, when the opposite is needed. Expectations: Not only is the development team excited at not having to work on the atrocious code you have now, but they are really looking forward to working on a product that has semi-modern design. The whole department is buzzing, and so is management! This causes your re-engineering effort to fail because the Chickens think that no only are you going to deliver perfect software, but you are going to deliver every feature and function of the old crappy product, as well as a handful of new and extraordinary features as well. And it’s unlikely that management will let you adjust the ship date to accommodate the new demands.

Rich highlighted a great post from Rocky DiStefano of Visible Risk in today’s Incite: Blame the addicts – When I was working at Gartner, nothing annoyed me more than those client calls where all they wanted me to do was read them the Magic Quadrant and confirm that yes, that vendor really is in the upper right corner. I could literally hear them checking their “talked to the analyst” box. An essential part of the due diligence process was making sure their vendor was a Leader, even if it was far from the best option for them. I guess no one gets fired for picking the upper right. Rocky DeStefano nails how people see the Magic Quadrant in his Tetragon of Prestidigitation post. Don’t blame the analyst for giving you what you demand – they are just giving you your fix, or you would go someplace else. – RM Rocky is dead on – there are a number of constituencies that leverage information like the Magic Quadrant, and they all have different perspectives on the report. I don’t need to repeat what Rocky said, but I want to add a little more depth about each of the constituencies and provide some anecdotes from my travels. To be clear, Gartner (and Forrester, for that matter) place all sorts of caveats on their vendor rankings. They say not to use them to develop a short list, and they want clients to call to discuss their specific issues. But here’s the rub: They know far too many organizations use the MQ as a crutch to support either their own laziness and stupidity, or to play the game and support decisions they’ve already made. Institutionally they don’t care. As Rich pointed out, (most of) the analysts hate it. But the vendor rankings represent enough revenue that they don’t want to mess with them. Yes, that’s a cynical view, but at the end of the day both of the big IT research shops are public companies and they have to cater to shareholders. And shareholders love licensing 10-page documents for $20K each to 10 vendors. Rocky uses 3 cases to illuminate his point, first a veteran information security professional, and those folks (if they have a clue) know that they’ve got to focus their short list on vendors close to the Leader Quadrant. If not, they’ll spend more time justifying another lesser-ranked vendor than implementing the technology. It’s just not worth the fight. So they don’t. They pick the best vendor from the leader quadrant and move on. This leads us to the second case, the executive, who basically doesn’t care about the technology, but has a lot of stuff on his/her plate and figures if a vendor is a leader, they must have lots of customers calling Gartner and their stuff can’t be total crap. Most of the time, they’d be right. And the third case is vendors. Rocky makes some categorizations about the different quadrants, which are mostly accurate. Vendors in the “niche” space (bottom left) don’t play into the large enterprise market, or shouldn’t be. Those in the “challenger” quadrant (top left) are usually big companies with products they bundle into broad suites, so the competitiveness of a specific offering is less important. Those in the “visionary” sector (bottom right) delude themselves into thinking they’ve got a chance. They are small, but Gartner thinks they understand the market. In reality it doesn’t matter because the vast majority of the market – dumb and/or lazy information security professionals – see the MQ like this: In most enterprise accounts the only vendors with a chance are the ones in the leader quadrant, so placement in this quadrant is critical. I’ve literally had CEOs and Sales VPs take out a ruler and ask why our arch-nemesis was 2mm to the right of our dot. 2 frackin millimeters. You may think I’m kidding, but I’m not. So many of the high-flying vendors make it their objective to spend whatever resources it takes to get into the leader quadrant. They have customers call into Gartner with inquiries about their selection process (even though the selection is already made) to provide data points about the vendor. Yes, they do that, and the vendors provide talking points to their clients. They show up at the conferences and take full advantage of their 1on1 meeting slots. They buy strategy days. To be clear, you cannot buy a better placement on the MQ. But you can buy access, which gives a vendor a better opportunity to tell their story, which in many cases results in better placement. Sad but true. Vendors can game the system to a degree. Which is why Rich, Adrian, and I made a solemn blood oath that we at Securosis would never do a vendor ranking. We’d rather focus our efforts on the folks who want advice on how to do their job better. Not those trying to maximize their Tetris time. Share:

Yesterday I gave this presentation as a webcast for McAfee, but somehow my last 8 slides got dropped from the deck. So, as promised, here is a PDF of the slides. McAfee is hosting the full webcast deck over at their blog. Since we don’t host vendor materials here at Securosis, here is the subset of my slides. (You might still want to check out their full deck, since it also includes content from an end user). Presentation: Quick Wins with DLP Share:

Mike Bailey highlights a key problem with web applications in his post on diversity. Having dealt with these issues as a web developer (a long time ago), I want to add a little color. We tend to talk about diversity as being good, usually with biological models and discussions of monoculture. I think Dan Geer was the first to call out the dangers of using only a single computing platform, since one exploit then has the capability of taking down your entire organization. But the heterogeneous/homogenous tradeoffs aren’t so simple. Diversity reduces the risk of a catastrophic single point of failure by increasing the attack surface and potential points of failure. Limited diversity is good for something like desktop operating systems. A little platform diversity can keep you running when something very bad hits the primary platform and takes those systems down. The trade off is that you now have multiple profiles to protect, with a great number of total potential vulnerabilities. For example, the Air Force standardized their Windows platforms to reduce patching costs and time. What we need, on the OS side, is limited diversity. A few standard platform profiles that strike the balance between reducing the risk that a single problem will take us completely down, while maintaining manageability through standardization. But back to Mike’s post and web applications… With web applications what we mostly see is false diversity. The application itself is a monolithic entity, but use of multiple frameworks and components only increases the potential attack surface. With desktop operating systems, diversity means a hole in one won’t take them all down. With web applications, use of multiple languages/frameworks and even platforms increases the number of potential vulnerabilities, since exploitation of any one of those components can generally take down/expose the entire application. When I used to develop apps, like every web developer at the time, I would often use a hodgepodge of different languages, components, widgets, etc. Security wasn’t the same problem then it is now, but early on I learned that the more different things I used, the harder it was to maintain my app over time. So I tended towards standardization as much as possible. We’re doing the same thing with our sooper sekret project here at Securosis – sticking to as few base components as we can, which we will then secure as well as we can. What Mike really brings to the table is the concept of how to create real diversity within web applications, as opposed to false diversity. Read his post, which includes things like centralized security services and application boundaries. Since with web applications we don’t control the presentation layer (the web browser, which is a ‘standard’ client designed to accept input from nearly anything out there), new and interesting boundary issues are introduced – like XSS and CSRF. Adrian and I talk about this when we advise clients to separate out encryption from both the application and the database, or use tokenization. Those architectures increase diversity and boundaries, but that’s very different than using 8 languages and widgets to build your web app. Share:

Thanks to Aza Raskin, this week we learned of a new phishing attack, dubbed “tabnabbing” by Brian Krebs. It opening a tab (unbeknownst to the user), changes the favicon, and does a great job of impersonating a web page – or a bank account, or any other phishing target. Through the magic of JavaScript, the tabs can be controlled and the attack made very hard to detect since it preys on the familiarity of users with common webmail and banking interfaces. So what do you do? You can run NoScript in your Firefox browser and to prevent the JavaScript from running (unless you idiotically allowed JavaScript on a compromised page). Another option is leveraging a password manager. Both Rich and I have professed our love for 1Password on the Mac. 1Password puts a button in your browser, and when logging in brings up a choice of credentials for that specific domain to automatically fill in the form. So when I go to Gmail, logging in is as easy as choosing one of the 4 separate logins I use on google.com domains. Now if I navigate to the phishing site, which looks exactly like Gmail, I’d still be protected. 1Password would not show me any stored logins for that domain, since presumably the phisher must use a different domain. This isn’t foolproof because the phisher could compromise the main domain, host the page there, and then I’m hosed. I could also manually open up 1Password and copy/paste the login credentials, but that’s pretty unlikely. I’d instantly know something was funky if my logins were not accessible, and I’d investigate. Both of these scenarios are edge cases and I believe in a majority of situations I’d be protected. I’m not familiar with password managers on Windows, but if they have similar capabilities, we highly recommend you use one. So not only can I use an extremely long password on each sensitive site, I get some phishing protection as a bonus. Nice. Share:

The first four posts our the SIEM series dealt with understanding what SIEM is, and what problems it solves. Now we move into how to select the right product/solution/service for your organization, and that involves digging into the technology behind SIEM and log management platforms. We start with the foundation of every SIEM and Log Management platform: data collection. This is where we collect data from the dozens of different types of devices and applications we monitor. ‘Data’ has a pretty broad meaning – here it typically refers to event and log records but can also include flow records, configuration data, SQL queries, and any other type of standard data we want to pump into the platform for analysis. It may sound easy, but being able to gather data from every hardware and software vendor on the planet in a scalable and reliable fashion is incredibly difficult. With over 20 vendors in the Log Management and SIEM space, and each vendor using different terms to differentiate their products, it gets very confusing. In this series we will define vendor-neutral terms to describe the technical underpinnings and components of log data collection, to level-set what you really need to worry about. In fact, while log files are what is commonly collected, we will use the term “data collection”, as we recommend gathering more than just log files. Data Collection Overview Conceptually, data collection is very simple: we just gather the events from different devices and applications on our network to understand what is going on. Each device generates an event each time something happens, and collects the events into a single repository known as a log file (although it could actually be a database). There are only four components to discuss for data collection, and each one provides a pretty straight-forward function. Here are the functional components: Fig 1. Agent data collector Fig 2. Direct connections to the device Fig 3. Log file collection Source: There are many different sources – including applications, operating systems, firewalls, routers & switches, intrusion detection systems, access control software, and virtual machines – that generate data. We can even collect network traffic, either directly from the network for from routers that support Netflow-style feeds. Data: This is the artifact telling us what actually happened. The data could be an event, which is nothing more than a finite number of data elements to describe what happened. For example, this might record someone logging into the system or a service failure. Minimum event data includes the network address, port number, device/host name, service type, operation being performed, result of the operation (success or error code), user who performed the operation, and timestamp. Or the data might just be configuration information or device status. In practice, event logs are pretty consistent across different sources – they all provide this basic information. But each offers additional data, including context. Additional data types may include things such as NetFlow records and configuration files. In practice, most of the data gathered will be events and logs, but we don’t want to arbitrarily restrict our scope. Collector: This connects to a source device, directly or indirectly, to collect the events. Collectors take different forms: they can be agents residing on the source device (Fig. 1), remote code communicating over the network directly with the device (Fig. 2), an agent writing code writing to a dedicated log repository (Fig. 3), or receivers accepting a log file stream. A collector may be provided by the SIEM vendor or a third party (normally the vendor of the device being monitored). Further, the collector functions differently, depending upon the idiosyncrasies of the device. In most cases the source need only be configured once, and events will be pushed directly to the collector or into a neutral log file read by it. In some cases, the collector must continually request data be sent, polling the source at regular intervals. Protocol: This is how collector communicates with the source. This is an oversimplification, of course, but think of it as a language or dialect the two agree upon for communicating events. Unfortunately there are lots of them! Sometimes the collector uses an API to communicate directly with the source (e.g., OPSEC LEA APIs, MS WMI, RPC, or SDEE). Sometimes events are streamed over networking protocols such as SNMP, Netflow, or IPFIX. Sometimes the source drops events into a common file/record format, such as syslog, Windows Event Log, or syslog-ng, which is then read by the collector. Additionally, third party applications such as Lasso and Snare provide these features as a service. Data collection is conceptually simple, but the thousands of potential variations makes implementation a complex mess. It resembles a United Nations meeting: you have a whole bunch of people talking in different languages, each with a particular agenda of items they feel are important, and different ways they want to communicate information. Some are loquacious and won’t shut up, while others need to be poked and prodded just to extract the simplest information. In a nutshell, it’s up to the SIEM and Log Management platforms to act as the interpreters, gathering the information and putting it into some useful form. Tradeoffs Each model for data collection has trade-offs. Agents can be a powerful proxy, allowing the SIEM platform to use robust (sometimes proprietary) connection protocols to safely and reliably move information off devices; in this scenario device setup and configuration is handled during agent installation. Agents can also take full advantage of native device features, and can tune and filter the event stream. But agents have fallen out of favor somewhat. SIEM installations cover thousands of devices, which means agents can be a maintenance nightmare, requiring considerable time to install and maintain. Further, agents’ processing and data storage requirements on the device can affect stability and performance. Finally, most agents require administrative access, which creates am additional security concern on each device. Another common technique streams events to log files, such as syslog or the Windows Event