After 26 days, and almost 350 million e-mail messages, only 28 sales resulted -- a conversion rate of well under 0.00001%. Of these, all but one were for male-enhancement products and the average purchase price was close to $100. Taken together, these conversions would have resulted in revenues of $2,731.88 -- a bit over $100 a day for the measurement period or $140 per day for periods when the campaign was active. However, our study interposed on only a small fraction of the overall Storm network -- we estimate roughly 1.5 percent based on the fraction of worker bots we proxy. Thus, the total daily revenue attributable to Storm's pharmacy campaign is likely closer to $7000 (or $9500 during periods of campaign activity). By the same logic, we estimate that Storm self-propagation campaigns can produce between 3500 and 8500 new bots per day.

Under the assumption that our measurements are representative over time (an admittedly dangerous assumption when dealing with such small samples), we can extrapolate that, were it sent continuously at the same rate, Storm-generated pharmaceutical spam would produce roughly 3.5 million dollars of revenue in a year. This number could be even higher if spam-advertised pharmacies experience repeat business. A bit less than "millions of dollars every day," but certainly a healthy enterprise.

Of course, the authors point out that it's dangerous to make these sorts of generalizations:

We would be the first to admit that these results represent a single data point and are not necessarily representative of spam as a whole. Different campaigns, using different tactics and marketing different products will undoubtedly produce different outcomes. Indeed, we caution strongly against researchers using the conversion rates we have measured for these Storm-based campaigns to justify assumptions in any other context.

Spam is all about economics. When sending junk mail costs a dollar in paper, list rental, and postage, a marketer needs a reasonable conversion rate to make the campaign worthwhile. When sending junk mail is almost free, a one in ten million conversion rate is acceptable.

News articles.

Lots being written about the Cloud, most of it quite dark and gloomy.  In fact I’m surprised, that Hoff hasn’t got a preso spooled up called “The Toxic Cloud” or something similarly ominous for his next speaking tour.
That said, the Economist does a great job distilling the issue into a simple statement -

Cloud computing is a trade-off between sovereignty and efficiency.

Let me ask you -  if you had to put your money on one of those horses, considering your average profit-preoccupied business, which would it be?  I’d put my bottom dollar on the thoroughbred named “Cost Center Reduction”, to place.

WHO ARE WE TO STAND IN THE WAY OF “PROGRESS”?

I’m always fond of Jack’s rule that the role of information risk management boils down to three deceptively simple premises:

• Reduce Risk.
• Reduce Loss.
• Create Operational Efficiencies.

So it would seem antithetical to the charter of the Chief Security Officer to stand in the way of progress as embodied by “cloud computing” (not to mention dangerous to long-term job security).  And I think that this presents opportunities to discuss strategies for managing risk, strategies that aren’t too theoretical and have practical application (though actual “cloud” use by enterprises may be rare at this point).

ON RISK REDUCTION IN THE CLOUD (or, How To Learn From the Shortcomings of PCI DSS)

The good news is, there’s already a well-established model for managing the risk around outsourcing the processing of “confidential” information.  The bad news is, that model kinda sucks it.

The Payment Card Industry, known as the “PCI” or “meal ticket” to many in the industry, faced a similar problem with the introduction of GLBA.  As I see it (and I’m not at all close to the PCI, at all, so this is all just abstract soliloquy) the PCI had one of two choices when faced with the prospect of other people managing their sensitive information:

1. Accept the *massive* amount of GLBA risk their business creates and spend a TON of money to build out the infrastructure (both process and IT) to manage the consumer data themselves (in conjunction with the banks, of course) and never have it grace the computing systems of the retailer.  Or,
2. Transfer the GLBA risk down to the retailer and have them bear the majority of the risk (and cost of reducing risk to a level that might be tolerable to the US Government).

(Martin, you may recall our Twittering about PCI a while back.  This is the crux of my view on the subj.)

Now fortunately, the CSO’s of the world are going to be a little more “invested” in protecting the information they are stewards over, and unlike the PCI, will remain primarily responsible for the C, I, & A of the data in the Cloud.  The cool thing is, this actually presents a great opportunity to start building a meaningful model for co-management of risk!  In fact, we can take the PCI model of contractual risk transference but modify where it goes all wrong, and start working to create something better.  And we can start by euthanizing some faulty assumptions.

JUST HOW INFORMATIVE IS PCI DSS?

What might be the.greatest.mistake of the standards compliance mentality is the assumption of value for the past-state measurement.  That is, I believe that the CSO needs more than some “past-state” assurance in order to understand their risk.    If you look at the concept of “PCI compliance” it really is an examination of a past state of nature that is assumed to be relevant to current and future states.   Many people (myself included) are not at all convinced that this past-state is nearly as informative as those who mandate it’s measurement believe it to be.

That’s not to condemn past-state measurements as completely non-informative,  they most certainly are useful.  It’s just that no self-respecting CSO sleeps well because they were deemed “PCI compliant” 10 months ago.  They sleep well because they have good visibility into current-state information and confidence in their strategy concerning future-state (based on that visibility and the outcomes of sound IRM models).

MOVING PAST THE VULNERABILITY SCANNER INTO INTELLIGENCE AND WISDOM

So realizing this new importance (to me, at least) concerning visibility and IRM models, I’m lead to the conclusion that if we are to manage risk in the Cloud, we’ll have to move beyond “PCI Compliance” or the concept that some regular “audit” of controls in place at the host is all we need to understand our ability to manage risk.  No, the CSO must have good information concerning current and probable future states.   This is that “visibility” I spoke of above.  In fact, we’ll need significant amounts of piercing, transparent visibility.  And in order to gain that visibility, our insight into Cloud Risk Management must include significant provisions for understanding a joint ability to Prevent/Detect/Respond as well as provisions for managing the risk that one of the participants won’t provide that visibility or ability via SLA’s and penalties . These SLA’s must be expressed in measurable terms (more visibility), and those metrics must have their roots in the things that help understand how we manage risk (those aforementioned IRM models).

THE CLOUD COMPUTING SECURITY SILVER LINING (sorry couldn’t resist)

As I mentioned earlier, I do see an opportunity to create insight.  The need for visibility and IRM models would allow us to create a “guidance” if you’ll allow me to use the term.  Not a standard or a “best practice” to audit by, but simply a reference document that says “if you’re going to put information on somebody else’s systems and still hold some significant responsibility for that information, here’s the considerations, why they are considerations, and how you might go about collaborating on the management of risk”.

And I think that if we undertake this journey, there is going to be a lot of growth and risk management innovation along the way.  But keen insights into what it means to manage risk will be necessary, and secure and forthright collaboration will be of absolute importance.

I say that last bit because, if these pundits are right about the utility of a hosted computing model - the Cloud will happen regardless of the CSO’s ability or desire to manage it.

Blogger: Dan Blum

With the crisis in financial markets still unfolding, it is important to draw what lessons we can from the experience. Since the roots of the crisis lie in a monumental failure of risk management, it’s important to understand more about what happened, and then draw some parallels to our business risk management and  IT risk management situations.

The risk management failure in the housing market and on Wall Street had multiple interdependent dimensions:

• Mortgage lenders abandoned long standing prudent loan practices. They made too many loans that buyers might not be able to repay. Exotic instruments like ARMs, option ARMs, and interest only loans proliferated. In many cases, all pretense of lending standards were abandoned, so-called “liar loans” approved.
• Capital was grossly over-leveraged. Mortgage lenders and other financial services packaged loans into securities, which they sold to raise capital to support more lending. Real capital reserve requirements to back loans were reduced. Of course, if borrowers could not repay loans, all or parts of the derivative securities would become worthless.
• Risk was aggregated at Fannie Mae, Freddie Mac, and mortgage loan insurance companies. These companies bought or insured some mortgage loans, providing something of a backstop should loans fail. Government sponsored enterprises (GSEs) Fannie and Freddie in turn became over-leveraged and securities that they sold were in turn repackaged in the murky brew of mortgage-backed securities called collateralized debt obligations (CDOs) and other exotic instruments returning generous yields.
• Non-Caveat Emptor. Institutional wealth funds and financial services firms who should have known better bought securities that had been deliberately structured to obfuscate risk. They bought securities they didn’t understand with buried tranches of toxic subprime loans..

It was a great Ponzi scheme – one that kept working as long as housing prices were going up; the recipients of subprime loans could always flip that house to the next buyer. Everyone made money. As Chuck Prince of Citigroup famously put it during a July, 2007 interview: “So long as the music is playing, you’ve got to keep dancing. We’re still dancing.” But one month later, the music stopped. Since then, Citigroup and other financial institutions have taken massive writeoffs with more to come. Wall Street titans like Bear Sterns, Lehman Brothers, Merrill Lynch, and AIG have fallen or been bought out.

What can we learn from this risk management debacle?

As business risk managers and investors, we should ask questions like these:

• Does the executive incentive structure of the company encourage managers to dance around risk? Many Wall Street firms paid senior managers 5 times their salary in bonuses tied to annual growth alone.
• Is the company over-leveraged? Is it borrowing too much money and betting it on ventures with uncertain outcomes?
• Are financial models used for risk management realistic? Earlier, I described the mortgage market of the past few years as a Ponzi scheme, where risk management models must have assumed prices would keep rising. Unlike the dotcom boom whose demise many predicted, very few in the industry foresaw the sharp declines to come in housing prices and sales volumes. Historically, the U.S. housing market has been a steadily rising one, but on the other hand the 2000s saw unprecedented rates of price increases. In reality, what goes up must come down.
• Has your company’s risk council ever performed worst case scenario analysis and built adequate reserves? In the days before economics emerged as a would-be “hard” deterministic science, business leaders may have been more cautious, more aware of and more accepting of uncertainty. Events like the Great Tulip Bubble came once in decades or centuries – not every few years. Note that legendary investor George Soros has proposed a Theory of Reflexivity that, if true, helps explain the recent extremes of boom and bust cycles. This theory holds that market participants model market behaviors based on self-interest, and for a time, their manipulations change the reality of the market – until gravitational forces bring it back to earth. Has the music of ephemeral success played to the backbeat of deterministic-sounding economic models gone to your heads and infected your risk management models?
• Are cost cutting efforts pursued blindly? Outsourcing and other forays into treacherous global waters may be giving away the crown jewels. Smart companies cut costs, but they do it in smart ways. Smart companies think like intelligence agencies as they parcel out work to different partners with varying levels of dependability, and they check on those partners.

Risk management failures can also occur at the more technical level of IT security. As IT risk managers, we might ask questions like these:

• Are the accounting and financial systems your IT department supports under adequate control? As Fred Cohen wrote in one of our documents: “Many companies use computers to manage financial systems, and despite the Sarbanes-Oxley Act (SOX) claims about accounts being properly kept, there are many attacks on financial systems that remain. For example, most of the largest financial systems in the world running on common financial databases do not use double-entry bookkeeping and are thus susceptible to all manner of frauds by insiders.” We find it troubling that a prudent control dating back to the 12th century is going out of style in the name of convenience and cost cutting. Kind of like credit checking became anachronistic during the housing bubble, eh?
• Is the “separation” in your “separation of duty” (SoD) for real? Sure the SOX auditors are looking for SoD, and maybe you have different administrators with different accounts maintaining different systems or functions. But when they say Western civilization may be but one weak password from collapse they’re not lying. Look what happened to Sarah Palin’s email account! Weak and straggly SoD is a problem across all critical IT systems where deperimiterization and server consolidation may be bringing down protective barriers, identity management is weak, and strong process controls (e.g., where two people must sign on, one perform a critical operation such as backbone router reconfiguration, and the second observe) abandoned in the name of expediency.
• Are risks being aggregated to unacceptable levels in centralized control systems? There are many ways that risks aggregate within enterprise IT infrastructures as we pursue automation and cost cutting. Network risks aggregate when centralized domain name system control is implemented. Application risks aggregate when common infrastructure is shared among applications. And enterprises aggregate platform risks when they use low-assurance endpoints, authentication, and directory systems with single sign-on to access large numbers of resources and don’t separate high consequence systems.
• Non-caveat emptor: Has IT security really done the worst case consequence analysis, attack graphs, and vulnerability analysis to know when putting more eggs in a supposedly stronger basket aggregates risks to an unacceptable level? Or are you depending only on vendor claims about some black box appliance equivalent of a risk-obfuscated CDO security? Caveat emptor (buyer beware) again! (The good news is we’ll keep talking about promoting vendor and product rating systems so you don’t have to do all the detailed product analysis yourself, but that’s another post.)

There are many parallels between the monumental risk management failure in the financial markets, and the probable weaknesses in our day to day business risk management and IT risk management. Abandonment of prudent practices for profit; excessive leverage and centralization; ill-constructed risk analysis models; risk obfuscation; and a failure of caveat emptor seem to be common problems. Please take this as a wakeup call to sharpen up the risk management thinking, process, and execution.

Now I’ve been “accused” of being a quant in the past (hi rybolov!) but in reality the only dogs I have in this fight are the model and the application of scientific method - and really, ethically speaking, I have to be tied to the latter while applying the former.

And I see a false dichotomy in this whole Quant vs. Qual thing.  We, as a profession, tend to create a political divide between the two which, if it even exists, I’d say is based more on our ignorance rather than our expertise.  After all, we are the profession that regularly multiplies across ordinal scales and uses wonderful models like R=VxTxI.   As someone  learning to deal in probabilities and rationalism, I have to recognize that this discussion is really just about the act of observation using different metrics of measurement.

But how we’re going about observing does not change the fact that there is measurement based on observation.  So if I’m working with you I can easily turn your qualitative scale into a quantitative one, and vice-versa.  Yes, Shrdlu, if we had the time, even your most seemingly Qual things could be Quant! (This flexible world view, btw, is an outcome of that new-fangled Bayesian thing).

COGNITIVE BIAS A-PLENTY

But back to what Rich is saying there about information security and risk - and he isn’t/won’t be the only one saying these sorts of things - we should try to understand what’s really going on rather than get caught up in the emotional hurricane.  Our profession suffers several forms of cognitive bias.  The nature of our jobs and what we do can cause us to be focused on the outcome and not the quality of the decision at the time it was made.  We want to bring in things from other professions that are useful, but at times we do view things outside our profession with false correlation to our own (unfortunately for those who write these sorts of articles, financial risk is completely different than operational risk).  We also have the tendency to focus on negative outcomes without acknowledging the positive outcomes (For example, I hear that Alan Greenspan’s new firm is up a couple of $billion in all this mess since he joined them, short sellers are doing quite well - must be because they have qualitative models or something -grin-).  The effect of these biases are compounded by the facts that proper correlation takes more work than we usually give it, and rational thought is not that easy when there’s a witch-hunt mentality.

What also floats in water? (link to Youtube)

WHAT SHOULD WE BE THINKING ABOUT?

So as you and I read opinions that seem to be the polar opposite of irrational exuberance (and there will be plenty between now and the election) we’ll have to ask ourselves, “what really failed here?”  At the risk (pun) of over-simplification:

• Was There an Error on the part of Probability Theory?

After all, Probability Science like all other fields of knowledge is always “advancing” as they say.  So perhaps probability theory is wrong somehow?

I’m personally disinclined to put the blame here, primarily because I would think that there would be evidence from other fields (like Quantum Mechanics) that something is amiss waaaaay before it hit a field like economics.

• Was There Error In The Model Used to Determine Risk?

Some people who understand real estate valuation and complex derivatives and financial risk want to put the blame here.  It’s a little too early to tell, but one thing is for sure - Financial risk is so different from operational risk I couldn’t begin to hazard an opinion on the subject.   But it would seem that this is really somewhere we might look.

• Was There Error In The  Scale Used (Quantitative vs. Qualitative)?

Honestly?  I find it extremely difficult to understand how this could be the source of financial ruin.

• Was There Error on the part of the Decision Maker?

What if all of the above were just fine, and the decision maker chose short term gain over long term stability?  What if this was (to simplify the matter greatly) a choice of “heads” over “tails” and the coin landed on tails?  What if the model represented the right risk (probability of negative outcome vs. positive outcome), but the complex derivative was sold to someone else who had poor “risk management” (ability to make a good decisions)?

Now I have no clue about complex derivatives, and I’m oversimplifying to be sure - chances are like most things, there are several problems that helped create the primary cause. But it seems to me that as we go into incident response mode for the economy, it’s more helpful to do so in a rational, logical manner.

OTHER THINGS WE MIGHT WANT TO CONSIDER

Consider the Source
Some authors (who I think tend to exploit outcome and hindsight bias,and then combine those with indirect ad hominem attacks in order to sell their books), are actually putting forth arguments against the use of analytics.  The source of this is a current epistemic debate between those who believe that only falsification is certain, and those who maintain that neither proof nor falsification are certain, there are only probabilities.    So before you go believing any “quadrants” of usefulness on faith - I encourage you to understand what is at the heart of the discussion.

We All Have to Live In The Real World
The sun will rise tomorrow, and someone will try to find the source of the problem and do a better job.  Now chances are, they’ll be doing it in a quantitative manner.  Chances are also that at some point their models will fail and we’ll need to build new ones.  And this will happen whether the field is cosmology, economics, meteorology, information security, or professional baseball.

WHAT ABOUT YOU, ALEX?

I’m far from certain and subject to change, but these days I lean towards Robin Hanson & MIchael Lewis w/regards to placing blame.

IBM’s presentation was cleverly titled 2mor0@Wrk - Tomororow work and Web 2.0.

Overview

Web 2.0 is delivering a whole different paradigm of communication. The slide is Lotus Symphony - NOT PPT. Over 2 million downloads.

There is an information overload that impacts individual productivity in the workplace. It has a profound effect on organizational productivity. A more complex organization entity provides more pressure and more inefficiencies in workplace. Up to 70% of time can be used looking for the WRONG information.

Collaboration mitigates information overload. It allows you to identify experts and opinions.

The collaboration agenda. Enterprises are at the onset of exploring these features. Web 2.0 is giving us the capacity to do more. Collaboration optimizes business outcomes - global, secure and dynamic.The most progressive companies are looking at UNIFIED COMMUNICATIONS. Making sure that directories and profiles are fully mobile.

Collaboration should be a contextual part of the workflow, going directly into applications.

IBM’s collaboration strategy is to deliver these services through online or offline services.

Demonstration

Executive IT architect Ron Sebastian provided a demonstration of IBM’s collaboration strategy. IBM’s Web 2.0 solutions span delivery platforms:

• Platform - web as  platform
• Application - development
• People - social computing

Lotus Connections - a family of social computing software that provides profile lookup and community capabilities. Think of Facebook, Yahoo Groups, and delicious combined in one portal.

Ron demonstrated these social services embedded into a healthcare provider application. Semantic tagging is available, contact information and commenting. Not only are we providing service to customers, you can integrate sync capability to directly call the person you want.

The biggest aspect of Lotus Connection? It’s all integrated.

A new service - Project Bluehouse. This is a SaaS delivery of these collaborated capabilities. The store and share can manage and share documents within and outside the company. Access control is no longer an issue.

Collaborative Web 2.0 services available as standalone products that also work in a mobile environment.

Case Study: Natural Disaster Management Mashup

Boeing came up with twenty different scenarios that they could handle through their systems. The problem was the one they didn’t count on. One example was Katrina - how to deliver supplies to the area: what airports were open? Where could they land? The problem was they could not find one list of public, private and military airports, nor what was open. The mashup took different feeds to allow the deacon maker to make a more rapid and intelligent decision based on information on where they could fly in the appropriate supplies. From open information sites like AirNav.com and personal contacts, users were able to mashup the information to make better decisions.

Conclusion

IBM announced the IBM Center for Social Software, proving their commitment to connect, collaborate, and innovate. Users and academics can work together to how these innovations can be applied to businesses and provide value to the market.

There has been some question of whether or not IBM can pull this off and move into the collaborative Web 2.0 market. Despite some criticism, it looks like IBM has really taken a step forward in advancing their products and services to meet market needs.

People drive better business outcomes. Connecting, collaboration, and innovation is key. Having the right tools and information to do that eases pressure that many organizations feel and brings Web 2.0 technologies to the heart of businesses.

A gulf exists between California consumers' understanding of online rules and common business practices. For instance, Californians who shop online believe that privacy policies prohibit third-party information sharing. A majority of Californians believes that privacy policies create the right to require a website to delete personal information upon request, a general right to sue for damages, a right to be informed of security breaches, a right to assistance if identity theft occurs, and a right to access and correct data.

These findings show that California consumers overvalue the mere fact that a website has a privacy policy, and assume that websites carrying the label have strong, default rules to protect personal data. In a way, consumers interpret "privacy policy" as a quality seal that denotes adherence to some set of standards. Website operators have little incentive to correct this misperception, thus limiting the ability of the market to produce outcomes consistent with consumers' expectations. Drawing upon earlier work, we conclude that because the term "privacy policy" has taken on a specific meaning in the minds of consumers, its use should be limited to contexts where businesses provide a set of protections that meet consumers' expectations.

adaptive process management (n.) an element of resource and business process management, adaptive search and event processing. Sometimes referred to as “Level 4” event processing or process refinement.

application concept (n.) a definition of a set of properties that represent the data fields of an application entity. An application concept can describe relationships among themselves. For example, an order concept might have a parent/child relationship with an item concept. A department concept might be related to a purchase requisition concept based on the shared property, department_id. Application concepts can include an application state model.

application state modeler (n.) a UML-compliant application that allows you to model the life cycle of a concept instance — that is, for each instance of a given concept, you can define which states it will pass through and how it will transition from state to state. States have entry actions, exit actions, and conditions, providing precision control over the behavior of an event processing agent. Transitions between states also may have rules. Multiple types of states and transitions maximize the versatility and power of the application state modeler.

derived event (n.) an event that is created as a result of processing one or more other events.

complex event (n.) an event that is a situation-entity abstraction of two or more simple, derived or other complex events.

complex event processing (n.) CEP is a technology for extracting information from message-based systems. CEP is primarily an event processing concept that deals with the task of processing multiple events from an event cloud with the goal of identifying the meaningful events within the event cloud. CEP employs techniques such as detection of complex patterns of many events, event correlation and abstraction, event hierarchies, and relationships between events such as causality, membership, and timing, and event-driven processes.

event (n.) a instance of an event definition. It is an immutable object that represents a business activity that happened at a single point in time. Just as one cannot change the fact that a given activity occurred, one cannot change an event — events are immutable.

event aggregation (n.) the aggregation of simple, derived or complex events into higher levels of event abstractions.

event definition (n.) a set of properties related to a given activity that represents an important or interesting change of state in a human, system or computational activity. An event definition includes event properties such as event priority, event time to live (TTL), and a description of the payload, which is comprehensive information related to the activity that occurred. Events expire when the TTL has elapsed, unless the event processing agent has instructions to consume them prior to that time.

event channel (n.) a communications channel in which events are transmitted from event source to event receivers, typically received as electronic messages. Each channel can have multiple destination and. events can be configured to transmit to a default destination. JMS is an example of an event channel.

event cloud (n.) a partially ordered set of events (poset), either bounded or unbounded, where the partial orderings are imposed by the causal, timing and other relationships between the events. Typically an event cloud is created by the events produced by one or more distributed systems. An event cloud may contain many event types, event streams and event channels. The difference between a cloud and a stream is that there is no event relationship that totally orders the events in a cloud.

event-driven (n.) the behavior of a human, system or computational entity whose execution or actuation is in response to events, typically received as electronic messages.

event-driven architecture (n.) an architectural style for distributed computing applications in which some of the components are event-driven and communicate by means of events.

event processing (n.) computing that performs operations on events, including modifying, creating and destroying events.

event-object (n.) an software object that represents an event, generally for the purpose of computer processing, that exhibits both encapsulation, inheritance and polymorphism.

event prediction (n.) computational activity where the impact of events, complex events, and situations caused by events identified, including both opportunity or threat. Sometimes referred to as “Level 2” event processing, impact assessment or predictive analytics.

event pre-processing (n.) computational activity where events are cleansed or normalized to produce semantically understandable data. Sometimes referred to as “Level 0” event processing.

event processing (n.) computational activities on events dealing with the association, correlation, and combination of event data and information from single and multiple event sources to achieve refined identity and situation estimates for observed event objects, and to achieve complete and timely assessments of opportunities, threats, and their significance. Event processing is characterized by continuous refinements of event estimates and assessments and by evaluation of the need for additional sources, or modification of the process itself, to achieve improved results.

event processing agent (n.) an EPA is a computational entity that performs event processing.

event processing network (n.) a set of event processing agents and a set of event channels connecting them.

event properties (n.) data representation of an event, typically by name-value pairs of type string, integer, real, boolean or a complex data type.

event refinement (n.) filter, identify and track events & make initial processing decisions based on association, correlation and state estimation. Sometimes referred to as “Level 1” event, or event-object, track and trace.

event stream (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

event stream processing (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

rule (n.) defines what triggers unusual, suspicious, problematic, or advantageous activity within an event processing agent and what the EPA does when it discovers these types of activities. Rules execute actions based on certain conditions on events, instances, or a combination of both. A rule includes a group of condition-rule statements and action-rule statements. The condition statements instruct the EPA what to look for in events, and action statements instruct the EPA how to respond when conditions are met. If all the conditions in a rule are satisfied by events or instances or both, the EPA fires the actions. The action might be to execute tasks, create an event instance, modify property values in an event instance, create and send an event, or something else.

rules engine (n.) a type of event processing agent that uses a declarative programming model to process events. Formally described as “an abstract structure that describes a formal language precisely, i.e., a set of rules that mathematically delineates a (usually infinite) set of finite-length strings over a (usually finite) alphabet“. Informally, it can be any system that uses rules, in any form, that can be applied to data to produce outcomes.

rule language (n.) is an artificial language that is used to control the behavior of an event processing agent. Rules languages, like human languages, have syntactic and semantic rules to define meaning.

situation refinement (n.) identify situations, or complex events, based on event clustering, event-event relationships and relationship analysis and context. Sometimes referred to as “Level 2” event processing.

simple event (n.) an event that is not an abstraction or composition of other events.

virtual event (n.) an event that is imagined, modeled or simulated.

With all this talk and reporting about security concerns, lets change the channel for a moment and assess the potential security benefits of Cloud Computing.

In my view, there are some strong technical security arguments in favour of Cloud Computing - assuming we can find ways to manage the risks.

With this new paradigm come challenges and opportunities.  The challenges are getting plenty of attention - I’m regularly afforded the opportunity to comment on them, plus obviously I cover them on this blog.  However, lets not lose sight of the potential upside.

In this post, I walk through seven technical security benefits.  Some are immediate, others may arise over time and have conditions attached (some unstated for the sake of brevity).  However, I’m including the longer-range benefits now to raise awareness.  Some of the outcomes listed are available today without the Cloud, but they are either complex and slow to implement (and thus less likely to happen) or prohibitive for capital cost reasons.  I don’t claim this is a definitive list - it reflects where my thinking is today.

Some benefits depend on the Cloud service used and therefore do not apply across the board.  For example; I see no solid forensic benefits with SaaS.  Also, for space reasons, I’m purposely not including the ‘flip side’ to these benefits, however if you read this blog regularly you should recognise some.

On a sidenote, I believe the Cloud offers Small and Medium Businesses major potential security benefits.  Frequently SMBs struggle with limited or non-existent in-house INFOSEC resources and budgets.  The caveat is that the Cloud market is still very new - security offerings are somewhat foggy - making selection tricky.  Clearly, not all Cloud providers will offer the same security.

Seven Technical Security Benefits of the Cloud

1. Centralised Data

• Reduced Data Leakage: this is the benefit I hear most from Cloud providers - and in my view they are right.  How many laptops do we need to lose before we get this?  How many backup tapes?  The data “landmines” of today could be greatly reduced by the Cloud as thin client technology becomes prevalent.  Small, temporary caches on handheld devices or Netbook computers pose less risk than transporting data buckets in the form of laptops.  Ask the CISO of any large company if all laptops have company ‘mandated’ controls consistently applied; e.g. full disk encryption.  You’ll see the answer by looking at the whites of their eyes.  Despite best efforts around asset management and endpoint security we continue to see embarrassing and disturbing misses.  And what about SMBs?  How many use encryption for sensitive data, or even have a data classification policy in place?
• Monitoring benefits: central storage is easier to control and monitor.  The flipside is the nightmare scenario of comprehensive data theft.  However, I would rather spend my time as a security professional figuring out smart ways to protect and monitor access to data stored in one place (with the benefit of situational advantage) than trying to figure out all the places where the company data resides across a myriad of thick clients!  You can get the benefits of Thin Clients today but Cloud Storage provides a way to centralise the data faster and potentially cheaper.  The logistical challenge today is getting Terabytes of data to the Cloud in the first place.

2. Incident Response / Forensics

• Forensic readiness: with Infrastructure as a Service (IaaS) providers, I can build a dedicated forensic server in the same Cloud as my company and place it offline, ready for use when needed.  I would only need pay for storage until an incident happens and I need to bring it online.  I don’t need to call someone to bring it online or install some kind of remote boot software - I just click a button in the Cloud Providers web interface.  If I have multiple incident responders, I can give them a copy of the VM so we can distribute the forensic workload based on the job at hand or as new sources of evidence arise and need analysis.  To fully realise this benefit, commercial forensic software vendors would need to move away from archaic, physical dongle based licensing schemes to a network licensing model.
• Decrease evidence acquisition time: if a server in the Cloud gets compromised (i.e. broken into), I can now clone that server at the click of a mouse and make the cloned disks instantly available to my Cloud Forensics server.  I didn’t need to “find” storage or have it “ready, waiting and unused” - its just there.
• Eliminate or reduce service downtime: Note that in the above scenario I didn’t have to go tell the COO that the system needs to be taken offline for hours whilst I dig around in the RAID Array hoping that my physical acqusition toolkit is compatible (and that the version of RAID firmware isn’t supported by my forensic software).  Abstracting the hardware removes a barrier to even doing forensics in some situations.
• Decrease evidence transfer time: In the same Cloud, bit fot bit copies are super fast - made faster by that replicated, distributed filesystem my Cloud provider engineered for me.  From a network traffic perspective, it may even be free to make the copy in the same Cloud.  Without the Cloud, I would have to a lot of time consuming and expensive provisioning of physical devices.  I only pay for the storage as long as I need the evidence.
• Eliminate forensic image verification time: Some Cloud Storage implementations expose a cryptographic checksum or hash.  For example, Amazon S3 generates an MD5 hash automagically when you store an object.  In theory you no longer need to generate time-consuming MD5 checksums using external tools - its already there.
• Decrease time to access protected documents: Immense CPU power opens some doors.  Did the suspect password protect a document that is relevant to the investigation?  You can now test a wider range of candidate passwords in less time to speed investigations.

3. Password assurance testing (aka cracking)

• Decrease password cracking time: if your organisation regularly tests password strength by running password crackers you can use Cloud Compute to decrease crack time and you only pay for what you use.  Ironically, your cracking costs go up as people choose better passwords ;-).
• Keep cracking activities to dedicated machines: if today you use a distributed password cracker to spread the load across non-production machines, you can now put those agents in dedicated Compute instances - and thus stop mixing sensitive credentials with other workloads.

4. Logging

• “Unlimited”, pay per drink storage: logging is often an afterthought, consequently insufficient disk space is allocated and logging is either non-existant or minimal.  Cloud Storage changes all this - no more ‘guessing’ how much storage you need for standard logs.
• Improve log indexing and search: with your logs in the Cloud you can leverage Cloud Compute to index those logs in real-time and get the benefit of instant search results. What is different here?  The Compute instances can be plumbed in and scale as needed based on the logging load - meaning a true real-time view.
• Getting compliant with Extended logging: most modern operating systems offer extended logging in the form of a C2 audit trail.  This is rarely enabled for fear of performance degradation and log size.  Now you can ‘opt-in’ easily - if you are willing to pay for the enhanced logging, you can do so.  Granular logging makes compliance and investigations easier.

5. Improve the state of security software (performance)

• Drive vendors to create more efficient security software: Billable CPU cycles get noticed.  More attention will be paid to inefficient processes; e.g. poorly tuned security agents.  Process accounting will make a comeback as customers target ‘expensive’ processes.  Security vendors that understand how to squeeze the most performance from their software will win.

6. Secure builds

• Pre-hardened, change control builds: this is primarily a benefit of virtualization based Cloud Computing.  Now you get a chance to start ’secure’ (by your own definition) - you create your Gold Image VM and clone away.  There are ways to do this today with bare-metal OS installs but frequently these require additional 3rd party tools, are time consuming to clone or add yet another agent to each endpoint.
• Reduce exposure through patching offline: Gold images can be kept up securely kept up to date.  Offline VMs can be conveniently patched “off” the network.
• Easier to test impact of security changes: this is a big one.  Spin up a copy of your production environment, implement a security change and test the impact at low cost, with minimal startup time.  This is a big deal and removes a major barrier to ‘doing’ security in production environments.

7. Security Testing

• Reduce cost of testing security: a SaaS provider only passes on a portion of their security testing costs.  By sharing the same application as a service, you don’t foot the expensive security code review and/or penetration test.  Even with Platform as a Service (PaaS) where your developers get to write code, there are potential cost economies of scale (particularly around use of code scanning tools that sweep source code for security weaknesses).

Your Thoughts?

What benefits do you see that I haven’t included in the above list?  Where do you agree/disagree and importantly, why?

For example, in my earlier post Modelling Situations for Event Processing, we illustrated modelling in CEP by looking at an example situation, “airplane collision”. This complex event is composed of many objects than are not event-objects. In fact, depending on how you define “event” most of the components of the complex event, “airplane collision” are not events at all, but other situations or sub-states of the objects under observation, in this case an aircraft.

For example, the direction an airplane is flying is not necessarily an “event” per se. Also, the amount of fuel on the aircraft at any given moment in time is not necessarily an “event” either. The same holds true for other components that comprise the object we are modelling. In fact, again depending on how you define “event”, most of the states of the components that are critical to processing a complex event are not events at all, they are simply object-states.

Complex events are generally composed of objects and the state of the complex event is defined by the objects in the complex event determined by the states of the components of the objects in the model.

Another way to view this key point is that CEP is characterised as predicting outcomes (states) based on the relationship between the objects in the model which are, in turn, composed of the states of various components of each of the objects.

So, in a nutshell, what is important to complex event processing is not just processing events, but processing the relative state of objects that comprise model of the complex event.

Furthermore, if you are someone who defines “event” as simply a “change of state,” stay tuned in to the blog for another discussion in a future post; because the vast amount of state changes are not events per se; they are simply changes in states which may or may not have context and meaning in complex event processing.

Having said that, a complex event can be comprised of other events, including other complex events, that is why the notion of OO modelling and programming is very important in CEP.