In the previous post, we addressed the importance of being able to parse the indications from the feed.
In order to match indications with external origin, you must have baselined you internal operations for the categories that interest you.
Let’s say you receive a report that suspected Guatemalan state-sponsored actor known as Sunshine Donkey (AKA Daft Burro / AKA Yummy Burrito) wiped hard drives at the Monterrey, Mexico facility of The Salsa Inc., a Mexico-based agri-food business, presumably in retaliation for Mexico’s policy to charge Guatemalan nationals higher rates for scuba diving permits.
The report includes malware file hashes.
The first thing your mega heavy cyber intel provider will have you do is grab the hashes and search your sensors. No hit = no problem. You are safe. Right?
A forward-looking approach would prompt you to ask the following questions as you attempt to match the external event against your own operations:
- Do any of the countries where I operate have ongoing scuba permit disputes with Guatemala?
- How easily could the same attack techniques be used to wipe my hard drives?
- Do I have any dealings with The Salsa Inc?
- Do I have any dealings in agri-foods?
- Do I have any facilities in Monterrey?
- Do I have any facilities in Mexico?
- Do I have Mexican suppliers (in this industry)?
- Do I have Mexican customers (in this industry)?
- Do I have any dealings in scuba?
- Do I have Guatemalan suppliers or customers (in this industry)?
- Do my key employees have plans to scuba dive in Mexico in the near future?
Now we are getting closer to proactive cyber risk intelligence. The key is to prepare to answer these questions before you get the report. More on that next time.
When you purchase intelligence feeds, you are generally purchasing flow emanating from the fire hose.
In order to be successful in matching the external environment to your internal situation, you must first be able to parse out or extract the atomic indication (with its relationship data) from the feed.
This means, If you are getting the feed as an email, you have to be able to identify the elements of the email that can be relevant to you. Without the ability to parse this out, you will seldom find a match. You can’t rely on your intel guy to read the entire fire hose flow, make sense of it, and make good warnings and recommendations.
If you are getting the feed as a JSON stream or an XML document or via API, you need to make sure that the atomic items important to you are readily accessible. If they are not, you will seldom find a match.
Finally, If you are only looking for IOCs (ignoring IOT, IOI, and IOO), you are only worrying about what has already happened in the past. This is important, but not the value you really want to get from your intelligence analyst.
The piece of information or intelligence that you get from an intelligence provider is known as an “indication”. For good and ill, these little guys have become a buzzword over the past 7 or so years. Pretty much every security practitioner today is familiar with the concept of “IOC” or “indication of compromise”.
Unfortunately the industry has been quite distracted and parochial about the broader concept of indications.
IOCs generally fall under what I call the “technical data” category. These are IP addresses, file hashes, email senders and subject lines, etc. Supposedly you can put these externally provided items into your internal sensors to see whether you were hit by the same adversaries.
IOCs are essentially reactive — that is, they are backwards looking. Ideally they were learned or observed as part of an attack that occurred somewhere else.
To be successful at cyber risk intelligence over the long term you need to expand beyond indications of compromise (IOCs) to also consider (or maybe even prioritize):
- Indications of targeting (IOT)
- Indications of adversary interest (IOI)
- Indications of adversary opportunity (IOO)
These types of indications can be extracted from the intelligence feed types noted previously:
- Technical data
- Assessment and Estimation
The next post will discuss the paramount importance of indication extraction.
Our last couple of posts introduced four types of intelligence product/reporting: Technical Data, TTPs, and Assessment and Estimation, and Vulnerability.
Intelligence or information in these categories is available from a variety of sources, including paid intelligence providers. Intelligence practitioners call incoming sources of information or intelligence “feeds”. But until you know what to do with them, you will waste vast amounts of money, time, and energy.
So here’s the secret: when reviewing feeds, analysts seek for matches between the external world and their internal systems across all four categories.
You will note that each risk intelligence type roughly corresponds to activities that can be considered operational, tactical, and strategic, respectively. In many cases, this also corresponds to a different security role or user within an organization. For example:
- Threat Hunters match technical data (such as attacker-controlled domain names) with data in internal sensor networks to identify compromises that have already occurred.
- Change Management Team matches TTP information and vulnerability disclosure information (such as an understanding of vulnerabilities exploited in attacks against other organizations) with software operated internally to prioritize patching or other mitigations.
- CISOs and CIOs match assessments and estimations of adversary capability (such as those drawn from long term planning documents and military doctrine of non-friendly nation-states) with their own operational geographies and industries.
Our previous posts established the groundwork for understanding how cyber risk intelligence allows organizations to answer the question “When will my organization be the victim of a significant cyber incident?”
When we last left off, we agreed to discuss four ways cyber risk intelligence analysts could match external or “threat” developments with internal systems the analyst desires to protect:
- Technical Data
- Tools, Techniques, and Procedures (TTPs)
- Assessment and Estimation (A&E)
- Vulnerability Discovery and Disclosure Data
To give you an idea of how each of these contributes to our objective of anticipating cyber incidents, I have labeled first three of them on the X axis of the Boom Chart.
You notice that Technical data is primarily reactive. It is generally gleaned from incident investigation.
TTPs are also learned from previous attacks, but carry forward due to the insight they provide about how an adversary operates.
Assessment & Estimation is forward looking based on a broad variety of factors that extend beyond bits and bytes level analysis.
The following image of the mind map (discussed previously) is color coded to indicate which elements fall under each category.
While this mind map is somewhat notional rather than complete and detailed: brown represents technical data, yellow represents TTPs, and beige represents estimation and assessment.