Hal Pomeranz, Deer Run Associates

At the end of our recent SANS webcast, Mike Poor closed by emphasizing how important it was for IT and Information Security groups to advertise their operational successes to the rest of the organization (and also to their own people).  Too often these functions are seen as pure cost centers, and in these difficult economic times it’s up to these organizations to demonstrate return value or face severe cutbacks.

The question is what are the right metrics to publish in order to indicate success?  All too often I see organizations publishing meaningless metrics, or even metrics that create negative cultures that damage corporate perception of the organization:

  • It seems like a lot of IT Ops groups like to publish their “look how much stuff we operate” metrics: so many thousand machines, so many petabytes of disk, terabytes of backup data per week, etc.  The biggest problem with these metrics is that they can be used to justify massive process inefficiencies.  Maybe you have thousands of machines because every IT project buys its own hardware and you’re actually wasting money and resources that could be saved by consolidating.  Besides, nobody else in the company cares how big your… er, server farm is.
  • Then there are the dreaded help desk ticket metrics: tickets closed per week, average time to close tickets, percentage of open tickets, etc.  The only thing these metrics do is incentivize your help desk to do a slapdash job and thereby annoy your customers.  There’s only one help desk metric that matters: customer satisfaction.  If you’re not doing customer satisfaction surveys on EVERY TICKET and/or you’re not getting good results then you fail.

So what are some good metrics?  Well I’m a Visible Ops kind of guy, so the metrics that matter to me are things like amount of unplanned downtime (drive to zero), number of successful changes requiring no unplanned work or firefighting (more is better), number of unplanned or unauthorized changes (drive to zero), and projects completed on time and on-budget (more is better).  Of course, if your IT organization is struggling, you might be tempted to NOT publish these metrics because they show that you’re not performing well.  In these cases, accentuate the positive by publishing your improvement numbers rather than the raw data: “This month we had 33% less unplanned downtime than last month.”  This makes your organization look proactive and creates the right cultural imperatives without airing your dirty laundry.

There are a couple of other places where I never fail to toot my own horn:

  • If my organization makes life substantially better for another part of the company then you’d better believe I’m going to advertise that fact.  For example, when my IT group put together a distributed build system that cut product compiles down from over eight hours to less than one hour, it not only went into our regular status roll-ups, but I also got the head of the Release Engineering group to give us some testimonials as well.
  • Whenever a significant new security vulnerability comes out that is not an issue for us because of our standard builds and/or operations environment, I make sure the people who provide my budget know about it.  It also helps if you can point to “horror story” articles about the amount of money other organizations have had to pay to clean up after incidents related to the vulnerability.  This is one of the few times that Information Security can demonstrate direct value to the organization, and you must never miss out on these chances.

What’s That Smell?

If communicating your successes builds a corporate perception of your organization’s value, being transparent about your failures builds trust with the rest of the business.  If you try to present a relentlessly positive marketing spin on your accomplishments your “customers” elsewhere in the company will become suspicious.  Plus you’ll never bamboozle them sufficiently with your wins that they won’t notice the elephant in the room when you fall on your face.

The important things to communicate when you fail are that you understand what lead to the failure, that you have the situational awareness to understand the impact of the failure on the business, and the steps you’re taking to make sure that the same failure never happens again (the only real organizational failure is allowing the same failure to happen twice).  Here’s a simple checklist of items you should have in your disclosure statement:

  • Analysis of the process(es) that led to the failure
  • The duration of the outage
  • How the outage was detected
  • The systems and services impacted
  • Which business units were impacted and in what way
  • Actions taken to end the outage
  • Corrective processes to make sure it never happens again

Note that in some cases it’s necessary to split the disclosure across 2-3 messages.  One is sent during the incident telling your constituents, “Yes, there’s a problem and we’re working it.”  The next is the “services restored at time X, more information forthcoming” message.  And then finally your complete post-mortem report.  Try to avoid partial or incomplete disclosure or idle speculation without all of the facts– you’ll almost always end up with egg on your face.

Conclusion

If you don’t communicate what’s happening in your IT and/or InfoSec organization then the the other business units are basically going to assume you’re not doing anything during the time when you’re not directly working on their requests. This leads to the perception of IT as nothing more than “revenue sucking pigs“.

However, you also have to communicate in the right way.  This means communicating worthwhile metrics and metrics which don’t create bad cultural imperatives for your organization.  And it also means being transparent and communicating your failures– in the most proactive way possible– to the rest of the organization.

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Calabrese’s Razor

February 26, 2009

Hal Pomeranz, Deer Run Associates

I’ve long held the opinion that the community of “Information Security Experts” agree with each other 90% of the time, but waste 90% of their time arguing to the death with other InfoSec Experts about the remaining 10%.  This was painfully brought home to me several years ago as I was facilitating the consensus process around the Solaris Security document published by the Center for Internet Security.  You won’t believe the amount of time we spent arguing about seemingly trivial things like, “Should the system respond to echo broadcast?”  And as the consensus circle widened, we ended up wasting more time on these issues and repeating debates over and over again as new people joined the discussion.  In short, it was killing us.  People were burning out and failing to provide constructive feedback and we were failing to deliver updates in a timely fashion.

I see these kind of debates causing similar mayhem in the IT Ops and InfoSec groups at many organizations.  The problem is that in these cases the organizations are not simply debating the content of a document full of security recommendations, they’re arguing about matters of operational policy.  This seems to promote even more irrational passions, and also raises the stakes for failing to come to consensus and actually move forward.

At the low point of our crisis at the Center for Internet Security, the person who was most responsible for finding the solution was Chris Calabrese, who was facilitating the HP-UX benchmark for the Center. At roughly the same time as our issues at the Center, the IT Ops and InfoSec teams at Chris’ employer had gotten bogged down over similar kinds of issues and had decided to come up with an objective metric for deciding which information security controls were important and which ones were just not worth arguing about.  Suddenly the discussion of these issues was transformed from matters of opinion to matters of fact.  Consensus arrived quickly and nobody’s feelings got hurt.

Overview of the Metric

So we decided to adapt the metric that Chris had used to our work at the Center.  After some discussion, we decided that the metric had to account for two major factors: how important the security control was and how much negative operational impact the security control would impose.  Each of the two primary factors was made up of other components.

For example, the factors relating to the relative importance of a security control include:

  • Impact (I): Is the attack just a denial-of-service condition, or does it allow the attacker to actually gain access to the system? Does the attack allow privileged access?
  • Radius (R): Does the attack require local access or can it be conducted in an unauthenticated fashion over the network?
  • Effectiveness (E): Does the attack work against the system’s standard configuration, or is the control in question merely a backup in case of common misconfiguration, or even just a “defense in depth” measure that only comes into play after the failure of multiple controls?

Similarly, the administrative impact of a control was assessed based on two factors:

  • Administrative Impact (A): Would the change require significant changes to current administrative practice?
  • Frequency of Impact (F): How regularly would this impact be felt by the Operations teams?

The equation for deciding which controls were important simply evolved to: “(I * R * E) – (A* F)”.  In other words, multiply the terms related to the importance of the control to establish a positive value and then subtract the costs due to the administrative impact of the control.

The only thing missing was the actual numbers.  It turns out a very simple weighting scheme is sufficient:

  • Impact (I): Score 1 if attack is a denial-of-service, 2 if the attack allows unprivileged access, and 3 if the attack allows administrative access (or access to an admin-equivalent account like “oracle”, etc)
  • Radius (R): Score 1 for attacks that require physical access or post-authenticated unprivileged access, and 2 for remote attacks that can be conducted by unauthenticated users
  • Effectiveness (E): Score 1 if the control requires multiple configuration failures to be relevant, 2 if the control is a standard second-order defense for common misconfiguration, and 3 if the attack would succeed against standard configurations without the control in question
  • Administrative Impact (A): Score 1 if the administrative impact is insignificant or none, 2 if the control requires modifications to existing administrative practice, and 3 if the control would completely disable standard administrative practices in some way
  • Frequency of Impact (F): Score 1 if the administrative impact is to a non-standard process or arises less than once per month, 2 if the administrative impact is to a standard but infrequent process that occurs about once per month, and 3 if the impact is to a regular or frequent administrative practice

In the case where a single control can have different levels of impact in different scenarios, what turned out best for us (and avoided the most arguments) was to simply choose the highest justifiable value for each term, even if that value was not the most common or likely impact.

Applying the Metric

Let’s run the numbers on a couple of controls and see how this works out.  First we’ll try a “motherhood and apple pie” kind of control– disabling unencrypted administrative access like telnet:

  • Impact (I): Worst case scenario here is that an attacker hijacks an administrative session and gains control of the remote system.  So that’s administrative level access, meaing a score of 3 for this term.
  • Radius (R): Anybody on the network could potentially perform this attack, so this term is set to 2.
  • Effectiveness (E): Again you have to go with the maximal rating here, because the session hijacking threat is a standard “feature” of clear-text protocols– score 3.
  • Administrative Impact (A): Remember, we’re not discussing replacing clear-text administrative protocols with encrypted protocols at this point (justifying encrypted access is a separate conversation).  We’re discussing disabling unencrypted access, so the score here is 3 because we’re planning on completely disabling this administrative practice.
  • Frequency of Impact (F): If telnet is your regular router access scheme, then this change is going to impact you every day.  Again, the score is then 3.

So what’s the final calculation?  Easy: (3 * 2 * 3) – (3 * 3) = 9.  What’s that number mean?  Before I answer that question, let’s get another point of comparison by looking at a more controversial control.

We’ll try my own personal nemesis, the dreaded question of whether the system should respond to echo broadcast packets:

  • Impact (I): Worst case scenario here ends up being a denial of service attack (e.g. “smurf” type attack), so score 1.
  • Radius (R): Depends on whether or not your gateways are configured to pass directed broadcast traffic (hint: they shouldn’t be), but let’s assume the worst case and score this one a 2.
  • Effectiveness (E): Again, being as pessimistic as possible, let’s assume no other compensating controls in the environment and score this one a 3.
  • Administrative Impact (A): The broadcast ping supporters claim that disabling broadcast pings makes it more difficult to assess claimed IP addresses on a network and capture MAC addresses from systems (the so-called “ARP shotgun” approach).  Work-arounds are available, however, so let’s score this one a 2.
  • Frequency of Impact (F): In this case, we have what essentially becomes a site-specific answer.  But let’s assume that your network admins use broadcast pings regularly and score this one a 3.

So the final answer for disabling broadcast pings is: (1 * 2 * 3) – (2 * 3) = 0.  You could quibble about some of the terms, but I doubt you’re going to be able to make a case for this one scoring any higher than a 2 or so.

Interpreting the Scores

Once we followed this process and produced scores for all of the various controls in our document, a dominant pattern emerged.  The controls that everybody agreed with had scores of 3 or better.  The obviously ineffective controls were scoring 0 or less.  That left items with scores in the 1-2 range as being “on the bubble”, and indeed many of these items were generating our most enduring arguments.

What was also clear was that it wasn’t worth arguing about the items that only came in at 1 or 2.  Most of these ended up being “second-order” type controls for issues that could be mitigated in other ways much more effectively and with much less operational impact.  So we made an organizational decision to simply ignore any items that failed to score at least 3.

As far as arguments about the weighting of the individual terms, these tended to be few and far between.  Part of this was our adoption of a “when in doubt, use the maximum justifiable value” stance, and part of it was due to choosing a simple weighting scheme that didn’t leave much room for debate.  Also, once you start plugging the numbers in, it’s obvious that arguing over a 1 point change in a single term isn’t usually enough to counteract the other factors enough to get a given control to reach the overall qualifying score of 3.

Further Conclusions

What was also interesting about this process is that it gave us an objective measure for challenging the “conventional wisdom” about various security controls.  It’s one thing to say, “We should always do control X”, and quite another to have to plug numbers for the various terms related to “control X” into a spreadsheet.  It quickly becomes obvious when a control has minimal security impact in the real world.

This metric also channelled our discussion into much more productive and much less emotional avenues.  Even the relatively coarse granularity of our instrument was sufficient to break our “squishy” matters of personal opinion into discrete, measurable chunks.  And once you get engineers talking numbers, you know a solution is going to emerge eventually.

So when your organization finds itself in endless, time-wasting discussions regarding operational controls, try applying Chris’ little metric and see if you don’t rapidly approach something resembling clarity.  Your peers will thank you for injecting a little sanity into the proceedings.

Chris Calbrese passed away a little more than a year ago from a sudden and massive heat attack, leaving behind a wife and children.  His insight and quiet leadership are missed by all who knew him.  While Chris developed this metric in concert with his co-workers and later with the input of the participants in the Center for Internet Security’s consenus process, I have chosen to name the metric “Calabrese’s Razor” in his memory.