Research

This is the final post in this series. If you want to track it through the entire editing process, you can follow along and contribute on GitHub. You can read the first post, and find the other posts under “related posts” in full article view. Choosing the Best Option There is no way to fully cover all the myriad factors in picking a specific encryption option in a (relatively) short paper like this, so we compiled a visual decision tree to at least get you into the right bucket. Here are a few notes on the decision tree. This isn’t exhaustive but should get you looking at the right set of technologies. In all cases you will want secure external key management. In general, for discreet data you want to encrypt as high in the stack as possible. When you don’t need as much separation of duties, encrypting lower may be easier and more cost effective. For both database and cloud encryption, in a few cases we recommend you encrypt in the application instead. When we list multiple options the order of preference is top to bottom. As you use this tree keep the Three Laws in mind, since they help guide the security value of your decision. Once you understand how encryption systems work, the different layers where you can encrypt, and how they combine to improve security (or not), it’s usually relatively easy to pick the right approach. The hard part is to then architect and implement the encryption technology and integrate it into your data center, application, or cloud service. That’s where our other encryption research can be valuable, and the following reports should help: Understanding and Selecting a Key Management Solution Pragmatic Key Management for Data Encryption Understanding and Selecting a Database Encryption or Tokenization Solution Defending Cloud Data with Infrastructure Encryption Understanding and Selecting a Tokenization Solution Understanding and Selecting Data Masking Solutions Share:

There is a ticker symbol HACK that tracks a group of publicly traded “Cyber Security” firms. Given how hot everything ‘Cyber’ is, HACK may do just fine – who knows? But perhaps one for breached companies (BRCH?) would be better. For you security geeks out there who love to talk about the cost of breaches, let’s take a look at the stock prices of several big-named firms which have been breached: Sony 11/24/14 28.3% S&P 500 11/24/14 2.2% Home Depot 9/9/14 31.3% S&P 500 9/9/14 6.4% Target 12/19/13 23.8% S&P 500 12/19/13 16.9% Heartland 1/20/09 250.1% S&P 500 1/20/09 162.7% Apple 9/2/14 28% S&P 500 9/2/14 6% This is a small sample of companies, but their stock values have each substantially outperformed the S&P 500 (which has been on a tear in the last year or so) from the time of their breaches through now. “How long until activist investors like Icahn pound the table demanding more dividends, stock buy backs and would it kill you to have a breach?” Food for thought. Share:

Rich here, Before I get into the cold open for this week, the past few days have been pretty nasty for privacy, security, and the digital supply chain. I will have a post on that up soon, but you can skip to the Top News section to catch the main stories. They are essential reading this week, and we don’t say that often. I am a ridiculous techno-addict, and have been my entire life. I suspect I inherited it from my father, who brought home an early microwave (likely responsible for my hair loss), video tape deck (where I watched Star Wars before VHS was on the market, the year the movie came out), and even a reel to reel videotape camera (black and white) I used for my own directorial debuts… often featuring my Star Wars figures. Gadgets have always been one of my vices, but as I have grown older they not only got cheaper, but also cheaper than what many of my 40+-year-old peers spend money on (cars, extra houses, extramarital partners for said houses, etc. ). That said, over time I have become a bit more discerning about where I drop money as I have come to better understand my own tastes and needs… and as my kids killed any semblance of hobby time. For this week’s Summary I thought I’d highlight a few of my current favorite gadgets. This isn’t even close to exhaustive – just a few current favorites. Logitech Harmony Ultimate Home + Hub – I don’t actually have all that crazy a TV setup, but it’s just complex enough that I wanted a universal remote. We switch a ton between our Apple TV and TiVo Roamio, and our kids are so that young regular remotes are a mess. The Harmony Ultimate is exactly what the name says. The remote itself is relatively small and has an adaptive touch screen that configures itself to the activity you are in. While it has an infrared transmitter like all remotes, it really uses RF to communicate to the Hub, which is located in our AV cabinet under the TV, and includes an IR blaster to hit all the components. This setup brings three key advantages. First, you don’t need to worry about where to point the remote. My kids would always lose aim in the middle of a multi-component command (something as simple as turning things on or off) and get frustrated. That’s no longer an issue. Second, the touch screen itself makes a cleaner remote with less buttons. You can prioritize the ones you use on the display, but still access all the obscure ones. Finally, the Hub is network enabled, and pairs with an iOS app. If I can’t find the remote I use my phone and everything looks and works the same. Because children. I have used earlier Logitech remotes and this is the first one that really delivers on all the promises. It is pricy, but futureproof, and even integrates with home automation products. I also got $80 off during a random Amazon sale. There isn’t anything else like this on the market, and I don’t regret it. We used our last Harmony remote for 7 years with our main TV, and it’s now in another room, so we got our money’s worth. Garmin Forerunner 920XT – I’m a triathlete. Not a great one by any means, but that’s my sport of choice these days. The Garmin 920XT was my holiday present this year, and it changed how I think about smartwatches. First, as a fitness tool, it is ridiculous. Aside from the GPS (and GLONASS – thank you, Russian friends), it connects with a ton of sensors, works as a basic smartwatch, and even includes an accelerometer – not only for step tracking, but also run tracking on treadmills and swim stroke tracking in pools. I didn’t expect to wear it every day but I do. Even getting simple notifications on my wrist means less pulling my phone out of my pocket, and I don’t worry about missing calls when I chase the kids during the work day and leave my phone on my desk. Yes, I’ll switch to an Apple Watch day-to-day when it comes out, but I went on a 17-mile run during working hours this week, and knowing I didn’t miss anything important was liberating. The 920XT is insane as a fitness tool. It will estimate your VO2 Max and predict race performance based on heart rate variability. It pulls in more metrics than you knew existed (or can use, but it makes us geeks happy). You can expand it with Garmin’s new ConnectIQ app platform. I added a half-marathon race predictor for my last race, and it helped me set a new PR – I am not great at math in the middle of a race. It walks me through structured workouts, then automatically uploads everything via my phone or home WiFi when I’m done, which then syncs to Strava and TrainingPeaks. If you aren’t a multisport athlete I’d check out the Fenix 3 or Vivoactive. They both support ConnectIQ. Neato XV-11 Robotic Vacuum – With multiple cats and allergies I was an early Roomba user. It worked well but had some key annoyances. It nearly never found its base to recharge, I’d have to remember to use the “virtual wall” infrared barriers to keep it in a room, and it was a royal pain to clean. Then I switched to the Neato XV-11 (an older model). It uses a stronger vacuum than the Roomba, is much easier to clean, maps rooms with LIDAR (laser radar), and nearly always finds its base to recharge. It is also much easier to schedule. The Neato will scan a room, clean until the battery gets low, go back to base, recharge, and then start out again up to 3 times (when it’s running on a schedule). It detects doorways automatically, stays in the room you put it in, and

This is the sixth post in a new series. If you want to track it through the entire editing process, you can follow along and contribute on GitHub. You can read the first post and find the other posts under “related posts” in full article view. Top Encryption Use Cases Encryption, like most security, is only adopted in response to a business need. It may be a need to keep corporate data secret, protect customer privacy, ensure data integrity, or satisfy a compliance mandate that requires data protection – but there is always a motivating factor driving companies to encrypt. The principal use cases have changed over the years, but these are still common. Databases Protecting data stored in databases is a top use case across mainframes, relational, and NoSQL databases. The motivation may be to combat data breaches, keep administrators honest, support multi-tenancy, satisfy contractual obligations, or even comply with state privacy laws. Surprisingly, database encryption is a relatively new phenomenon. Database administrators historically viewed encryption as carrying unacceptable performance overhead, and data security professionals viewed it as a redundant control – only effective if firewalls, identity management, and other security measures all failed. Only recently has the steady stream of data breaches shattered this false impression. Combined with continued performance advancements, multiple deployment options, and general platform maturity, database encryption no longer carries a stigma. Today data sprawls across hundreds of internal databases, test systems, and third-party service providers; so organizations use a mixture of encryption, tokenization, and data masking to tailor protection to each potential threat – regardless of where data is moved and used. The two best options for encrypting a database are encrypting data fields in the application before sending to the database and Transparent Database Encryption. Some databases support field-level encryption, but the primary driver for database encryption is usually to restrict database administrators from seeing specific data, so organizations cannot rely on the database’s own encryption capabilities. TDE (via the database feature or an external tool) is best to protect this data in storage. It is especially useful if you need to encrypt a lot of data and for legacy applications where adding field encryption isn’t reasonable. For more information see Understanding and Selecting a Database Encryption or Tokenization Solution. Cloud Storage Encryption is the main data security control for cloud computing. It enables organizations to maintain control over data security, even in multitenant environments. If you encrypt data, and control the key, even your cloud provider cannot access it. Unfortunately cloud encryption is generally messy for SaaS, but there are decent options to integrate encryption into PaaS, and excellent ones for IaaS. The most common use cases are encrypting storage volumes associated with applications, encrypting application data, and encrypting data in object storage. Some cloud providers are even adding options for customers to manage their own encryption keys, while the provider encrypts and decrypts the data within the platform (we call this Bring Your Own Key). For details see our paper on Defending Cloud Data with Infrastructure Encryption. Compliance Compliance is a principal driver of encryption and tokenization sales. Some obligations, such as PCI, explicitly require it, while others provide a “safe harbor” provision in case encrypted data is lost. Typical policies cover IT administrators accessing data, users issuing ad hoc queries, retrieval of “too much” information, or examination of restricted data elements such as credit card numbers. So compliance controls typically focus on issues of privileged user entitlements (what users can access), segregation of duties (so admins cannot read sensitive data), and the security of data as it moves between application and database instances. These policies are typically enforced by the applications which process users requests, limiting access (decryption) according to policy. Policies can be as simple as allowing only certain users to see certain types of data. More complicated policies build in fraud deterrence, limit how many records specific users are allowed to see, and shut off access entirely in response to suspicious user behavior. In other use cases, where companies move sensitive data to third-party systems they do not control, data masking and tokenization have become popular choices for ensuring sensitive data does not leave the company at all. Payments The payments use case deserves special mention; although commonly viewed as an offshoot of compliance, it is more a backlash – an attempt to avoid compliance requirements altogether. Before data breaches it was routine to copy payment data (account numbers and credit card numbers) anywhere they could possibly be used, but now each copy carries the burden of security and oversight, which costs money. Lots of it. In most cases payment data was not required, but the usage patterns based around it became so entrenched that removal would break applications. For example merchants do not need to store – or even see – customer credit card numbers for payment, but many of their IT systems were designed around credit card numbers. In the payment use case, the idea is to remove payment data wherever possible, and thus the threat of data breach, thus reducing audit responsibility and cost. Here tokenization, format-preserving encryption, and masking have come into their own: removing sensitive payment data, and along with it most need for security and compliance. Industry organizations like PCI and regulatory bodies have only recently embraced these technical approaches for compliance scope reduction, and more recent variants (including Apple Pay merchant tokens) also improve user data privacy. Applications Every company depends on applications to one degree or another, and these applications process data critical to the business. Most applications, be they ‘web’ or ‘enterprise’, leverage encryption. Encryption capabilities may be embedded in the application or bundled with the underlying file system, storage array, or relational database system. Application encryption is selected when fine-grained control is needed, to encrypt select data elements, and to only decrypt information as appropriate for the application – not merely because recognized credentials were provided. This granularity of control comes at a price – it is more

This is the fifth post in a new series. If you want to track it through the entire editing process, you can follow along and contribute on GitHub. You can read the first post and find the other posts under “related posts” in full article view. Additional Platform Features and Options The encryption engine and the key store are the major functional pieces in any encryption platform, but there are supporting systems with any data center encryption solution that are important for both overall management, as well as tailoring the solution to fit within your application infrastructure. We frequently see the following major features and options to help support customer needs: Central Management For enterprise-class data center encryption you need a central location to define both what data to secure and key management policies. So management tools provide a window onto what data is encrypted and a place to set usage policies for cryptographic keys. You can think of this as governance of the entire crypto ecosystem – including key rotation policies, integration with identity management, and IT administrator authorization. Some products even provide the ability to manage remote cryptographic engines and automatically apply encryption as data is discovered. Management interfaces have evolved to enable both security and IT management to set policy without needing cryptographic expertise. The larger and more complex your environment, the more critical central management becomes, to control your environment without making it a full-time job. Format Preserving Encryption Encryption protects data by scrambling it into an unreadable state. Format Preserving Encryption (FPE) also scrambles data into an unreadable state, but retains the format of the original data. For example if you use FPE to encrypt a 9-digit Social Security Number, the encrypted result would be 9 digits as well. All commercially available FPE tools use variations of AES encryption, which remains nearly impossible to break, so the original data cannot be recovered without the key. The principal reason to use FPE is to avoid re-coding applications and re-structuring databases to accommodate encrypted (binary) data. Both tokenization and FPE offer this advantage. But encryption obfuscates sensitive information, while tokenization removes it entirely to another location. Should you need to propagate copies of sensitive data while still controlling occasional access, FPE is a good option. Keep in mind that FPE is still encryption, so sensitive data is still present. Tokenization Tokenization is a method of replacing sensitive data with non-sensitive placeholders: tokens. Tokens are created to look exactly like the values they replace, retaining both format and data type. Tokens are typically ‘random’ values that look like the original data but lack intrinsic value. For example, a token that looks like a credit card number cannot be used as a credit card to submit financial transactions. Its only value is as a reference to the original value stored in the token server that created and issued the token. Tokens are usually swapped in for sensitive data stored in relational databases and files, allowing applications to continue to function without changes, while removing the risk of a data breach. Tokens may even include elements of the original value to facilitate processing. Tokens may be created from ‘codebooks’ or one time pads; these tokens are still random but retain a mathematical relationship to the original, blurring the line between random numbers and FPE. Tokenization has become a very popular, and effective, means of reducing the exposure of sensitive data. Masking Like tokenization, masking replaces sensitive data with similar non-sensitive values. And like tokenization masking produces data that looks and acts like the original data, but which doesn’t pose a risk of exposure. But masking solutions go one step further, protecting sensitive data elements while maintaining the value of the aggregate data set. For example we might replace real user names in a file with names randomly selected from a phone directory, skew a person’s date of birth by some number of days, or randomly shuffle employee salaries between employees in a database column. This means reports and analytics can continue to run and produce meaningful results, while the database as a whole is protected. Masking platforms commonly take a copy of production data, mask it, and then move the copy to another server. This is called static masking or “Extract, Transform, Load” (ETL for short). A recent variation is called “dynamic masking”: masks are applied in real time, as data is read from a database or file. With dynamic masking the original files and databases remain untouched; only delivered results are changed, on-the-fly. For example, depending on the requestor’s credentials, a request might return the original (real, sensitive) data, or a masked copy. In the latter case data is dynamically replaced with a non-sensitive surrogate. Most dynamic masking platforms function as a ‘proxy’ something like firewall, using redaction to quickly return information without exposing sensitive data to unauthorized requesters. Select systems offer more intelligent randomization, tokenization, or even FPE. Again, the lines between FPE, tokenization, and masking are blurring as new variants emerge. But tokenization and masking variants offer superior value when you don’t want sensitive data exposed but cannot risk application changes. Share:

This is the fourth post in a new series. If you want to track it through the entire editing process, you can follow along and contribute on GitHub. You can read the first post and find the other posts under “related posts” in full article view. Key Management Options As mentioned back in our opening, the key (pun intended – please forgive us) to an effective and secure encryption system is proper placement of the components. Of those the one that most defines the overall system is the key manager. You can encrypt without a dedicated key manager. We know of numerous applications that take this approach. We also know of numerous applications that break, fail, and get breached. You will nearly always want to use a dedicate key management option, which breaks down into four types: The first thing to consider is how to deploy external key management. There are four options: An HSM or other hardware key management appliance. This provides the highest level of physical security. It is the most common option in sensitive scenarios, such as financial services and payments. The HSM or appliance runs in your data center, and you always want more than one for backup. Lose access and you lose your keys. Apple, for example, has stated publicly that they physically destroy the administrative access smart cards after configuring a new appliance so no one can ever access and compromise the keys, which are destroyed if someone tries to open the housing or certain other access methods. A hardware root of trust is the most secure option, and all those products also include hardware acceleration for cryptographic operations to improve performance. A key management virtual appliance. A vendor provides a pre-configured virtual appliance (instance) for you to run where you need it. This reduces costs and increases deployment flexibility, but isn’t as secure as dedicated hardware. If you decide to go this route, use a vendor who takes exceptional memory protection precautions, because there are known techniques for pulling keys from memory in certain virtualization scenarios. A virtual appliance doesn’t offer the same physical security as a physical server, but they do come hardened, and support more flexible deployment options – you can run them within a cloud or virtual datacenter. Some systems also allow you to use a physical appliance as the hardware root of trust for your keys, but then distribute keys to virtual appliances to improve performance in distributed scenarios (for virtualization or simply cost savings). Key management software, which can run either on a dedicated server or within a virtual/cloud server. The difference between software and a virtual appliance is that you install the software yourself rather than receiving a hardened and configured image. Otherwise software offers the same risks and benefits as a virtual appliance, assuming you harden the server as well as the virtual appliance. Key Management Software as a Service (SaaS). Multiple vendors now offer key management as a service specifically to support public cloud encryption. This also works for other kinds of encryption, including private clouds, but most usage is for public clouds. Client Access Options Whatever deployment model you choose, you need some way of getting keys where they need to be, when they need to be there, for cryptographic operations. Clients (whatever needs the key) usually need support for the following core functions for a complete key management lifecycle: Key generation Key exchange (gaining access to the key) Additional key lifecycle functions, such as expiring or rotating a key Depending on what you are doing, you will allow or disallow these functions under different circumstances. For example you might allow key exchange for a particular application, but not allow it any other management functions (such as generation and rotation). Access is managed one of three ways, and many tools support more than one: Software Agent: A dedicated agent handles client key functions. These are generally designed for specific use cases – such as supporting native full disk encryption, specific backup software, various database platforms, and so on. Some agents may also perform cryptographic functions for additional hardening, such as wiping the key from memory after each use. Application Programming Interfaces: Many key managers are used to handle keys from custom applications. An API allows you to access key functions directly from application code. Keep in mind that APIs are not all created equal – they vary widely in platform support, programming languages supported, simplicity or complexity of API calls, and the functions accessible via the API. Protocol & Standards Support: The key manager may support a combination of proprietary and open protocols. Various encryption tools support their own protocols for key management, and like software agents, the key manager may include support – even if it is from a different vendor. Open protocols and standards are also emerging but not yet in wide use, and may be supported. We have written a lot about key management in the past. To dig deeper take a look at Pragmatic Key Management for Data Encryption and Understanding and Selecting a Key Management Solution. Share:

I meant to write about this earlier and forgot. Last week I was listening to the Diane Rehm show on NPR while out for a long run (I am weird and prefer talk radio/podcasts on long workouts). The show was all about cybersecurity. To be honest, the panel was a bit weak (Ravi Pendse from Brown was decent). When they opened up the phone lines, as you would expect, a lot of consumers called in. I will paraphrase one call a bit.. I don’t really get what the big deal is. If someone uses my Social Security number all I need to do is call my bank and clean it up. This was prefaced by: I’ve worked on process control systems for over 20 years, like water treatment and other utilities. Even the panel had a hard time responding. (Sorry I don’t have a transcript.) Share:

Last week President Obama held a cybersecurity summit out in the Bay Area. He issued a new executive order and is standing up a new threat sharing center. This is in response to ongoing massive attacks such as the Anthem breach and (as we heard this weekend) hundreds of millions stolen in bank thefts. But what does it all mean to security pros and the industry? The truth is, not much in our day-to-day (yet), but you certainly had better pay attention. Watch or listen: Share:

Picture enterprise applications as a layer cake: applications sit on databases, databases on files, and files are mapped onto storage volumes. You can use encryption at each of these layers in your application stack: within the application, in the database, on files, or on storage volumes. Where you use an encryption engine dominates security and performance. Higher up the stack can offer more security, with higher complexity and performance cost. There is a similar tradeoff with encryption engine and key manager deployments: more tightly coupled systems offer less complexity, but less security and reliability. Building an encryption system requires a balance between security, complexity, and performance. Let’s take a closer look at each layer and their tradeoffs. Application Encryption One of the more secure ways to encrypt application data is to collect it in the application, send it to an encryption server or appliance (an encryption library embedded in the application), and then store the encrypted data in a separate database. The application has full control over who sees what and can secure data without depending on the security of the underlying database, file system, or storage volumes. The keys themselves might be on the encryption server or could even be stored in yet another system. The separate key store increases security, simplifies management of multiple encryption appliances, and helps keep keys safe for data movement – backup, restore, and migration/synchronization to other data centers. Database Encryption Relational database management systems (RDBMS) typically have two encryption options: transparent and column. In our layer cake above columnar encryption occurs as applications insert data into a database, whereas transparent encryption occurs as the database writes data out. Transparent encryption is applied automatically to data before it is stored at the file or disk layer. In this model encryption and key management happen behind the scenes, without the user’s knowledge or requiring application programming. The database management system handles encryption and decryption operations as data is read (or written), ensuring all data is secured, and offering very good performance. When you need finer control over data access, you can encrypt single columns, or tables, within the database. This approach offers the advantage that only authenticated users of encrypted data are able to gain access, but requires changing database or application code to manage encryption operations. With either approach there is less burden on application developers to build a crypto system, but slightly less control over who can access sensitive data. Some third-party tools also offer transparent database encryption by automatically encrypting data as it is stored in files. These tools aren’t part of the database management system itself, so they can work with databases that don’t support TDE directly, and provide greater separation of duties for database administrators. File Encryption Some applications, such as payment systems and web applications, do not use databases and instead store sensitive data in files. Encryption is applied transparently as data is written to files. This type of encryption is offered as a third-party add-on to the file system, or embedded within the operating system. Encryption and decryption are transparent to both users and applications. Data is decrypted when a user requests a file, after they have authenticated to the system. If the user does not have permission to read the file, or has not provided proper credentials, they only get encrypted data. File encryption is commonly used to protect “data at rest” in applications that do not include encryption capabilities, including legacy enterprise applications and many big data platforms. Disk/Volume Encryption Many off-the-shelf disk drives and Storage Area Network (SAN) arrays include automatic data encryption. Encryption is applied as data is written to disk, and decrypted by authenticated users/apps when requested. Most enterprise-class systems hold encryption keys locally to support encryption operations, but rely on external key management services to manage keys and provide advanced key services such as key rotation. Volume encryption protects data in case drives are physically stolen. Authenticated users and applications are provided unencrypted copies of files and data. Tradeoffs In general, the further “up the stack” you deploy encryption, the more secure your data is. The price of that extra security is more difficult integration, usually in the form o application code changes. Ideally we would encrypt all data at the application layer and fully leverage user authentication, authorization, and business context to determine who can see sensitive data. In the real world the code changes required for this level of precision control are often insurmountable engineering challenges and/or cost prohibitive. Surprisingly, transparent encryption often perform faster than application-layer encryption, even with larger data sets. The tradeoff is moving high enough “up the stack” to address relevant threats while minimizing the pain of integration and management. Later in this series we will walk you through the selection process in detail. Next up in this series: key management options. Share:

Welcome to the Friday the 13th edition of the Friday Summary! It has been a while since I wrote the summary so there is lots to cover … My favorite external post this week is a research paper, Mongo Databases At Risk, outlining a very common trend among MongoDB users: not using basic user authentication to secure their databases. Well, that, and putting them on the Internet. On the default port. Does this sound like SQL Server circa 2003 to anyone else? One angle I found important was the number of instances discovered: nearly 40k databases. That is a freakin’ lot! Remember, this is MongoDB. And just those running on the Internet at the default port. Yes, it’s one of the top NoSQL platforms, but during our inquiries we spoke with 4 Hadoop users for every MongoDB user. MongoDB was also behind Hadoop and Cassandra. I don’t know if anyone publishes download or usage numbers for the various platforms, but extrapolating from those numbers, there are a lot of NoSQL databases in use. Someone with more time on their hands might decide to scan the Internet for instances of the other platforms (the default port for Hadoop, Cassandra, CouchDB, and Redis is 6380; RIAK is 8087). I would love to know what you find. Back to security… I have had conversations with several firms trying to figure out how to monitor NoSQL usage; we know how to apply DAM principles to SQL, but MapReduce and other types of queries are much more difficult to parse. I expect several vendors to introduce basic monitoring for Hadoop in the next year, but it will take time to mature, and even more to cover other platforms. What I haven’t heard discussed is the easier – and no less pressing – issue of configuration and vulnerability assessment. The enterprise distributions are providing best practices but automated scans are scarce – and usually custom. That is a free hint for any security vendors looking for a quick way to help big data customers get secure. Mobile security consumes much more of my time than it should. I geek out on it, often engaging Gunnar in conversation on everything from the inner workings of secure elements to the apps that make payments happen. And I read everything I can find. This week I ran across Why Banks Will Win the Battle for the Mobile Wallet, by John Gunn – the guy who runs the wonderfully helpful twitter feed @AuthNews. But on this I think he has missed the point. Banks are not battling to win mobile wallets. In fact those I have spoken with don’t care about wallets. They care about transactions. And moving more transactions from cash to credit means a growing stream of revenue for merchant banks and payment processors, which makes them very happy. Wallets in and of themselves don’t fosters adoption – as Google is well aware – and in fact many users don’t really trust wallets at all. What gets people to move from a plastic card or cash, at least in the US, is a combination of convenience and trust. Starbucks leveraged their brand affinity into seven million subscribers for their app and an impressive 2.1 million transactions per week. Banks benefit directly when more transactions move away from cash, and they are happy to let others own the user experience. But things get really interesting in overseas markets, which make US adoption of mobile payments look like a payments backwater. Nations without traditional banking or payment infrastructure can now move money in ways they previously could not, so adoption rates have been huge. Leveraging cellular infrastructure makes it faster and safer to move money, with fewer worries about carrying cash. Nations like Kenya – which is not often considered on the cutting edge of technology, but had 25 million mobile payment users and moved $26 billion in 2014 via mobile payments and mobile money subscriptions. Sometimes technology really does make the world a better place. The banks don’t care which wallets, apps, technology, or carriers wins – they just want someone to make progress. In January I normally publish my research calendar for the coming year. But Rich has been hogging the Friday Summary for weeks now, so I finally get a chance to talk about what I am seeing and doing. Tokenization: I am – finally – going to publish some thoughts on the latest trends in tokenization. I want to talk about changes in the technology, adoption on mobile platforms, how the latest PCI specification is changing compliance, and some customer user cases. Risk-Based Authentication and Authorization: We see many more organizations looking at risk-based approaches to augment the security of web-based applications. Rather than rewrite applications they use metadata, behavioral information, business context, and… wait for it… big data analytics to better determine the acceptability of a request. And it is often cheaper and easier to bolt this on externally than to change applications. Gunnar and I have wanted to write this paper for a year, and now we finally have the time. Building a Security Analytics Platform: I have been briefed by many of security analytics startups, and each is putting together some basic security analysis capabilities, usually built on big data databases. I have, in that same period, also spoken with many large enterprises who decided not to wait for the industry to innovate, and are building their own in-house systems. The last couple even asked me what I thought of certain architectural choices, and which data elements should they use as hash keys! So there is considerable demand for consumer education; I will cover off-the-shelf and DIY options. I am still on the fence about some secure code development ideas, so if you have an idea, let’s talk. Even the security vendors I have visited in the last year have pulled me aside to ask about transitioning to Agile, or how to fix a failed transition to Agile. Most want to know what