When a client asked me to help build a cyberthreat intelligence program recently, I jumped at the opportunity to try something new and challenging. To begin, I set about looking for some rudimentary templates with a good outline for building a threat intelligence process, a few solid platforms that are user-friendly, the basic models for cyber intelligence collection and a good website for describing various threats an enterprise might face. This is what I found:
There are a handful of rudimentary templates for building a good cyberthreat intelligence program available for free online. All of these templates leave out key pieces of information that any novice to the cyberthreat intelligence field would be required to know. Most likely, this is done to entice organizations into spending copious amounts of money on a specialist.
The number of companies that specialize in the collection of cyberthreat intelligence is growing at a ludicrous rate, and they all offer something that is different, unique to certain industries, proprietary, automated via artificial intelligence (AI) and machine learning, based on pattern recognition, or equipped with behavioral analytics.
The basis for all threat intelligence is heavily rooted in one of three basic models: Lockheed Martin’s Cyber Kill Chain, MITRE’s ATT&CK knowledge base and The Diamond Model of Intrusion Analysis.
A small number of vendors working on cyberthreat intelligence programs or processes published a complete list of cyberthreats, primary indicators, primary actors, primary targets, typical attack vectors and potential mitigation techniques. Of that small number, very few were honest when there was no useful mitigation or defensive strategy against a particular tactic.
All of the cyberthreat intelligence models in use today have gaps that organizations will need to overcome.
A search within an article content engine for helpful articles with the keyword “threat intelligence” produced more than 3,000 results, and a Google search produces almost a quarter of a million. This is completely ridiculous. Considering how many organizations struggle to find experienced cyberthreat intelligence specialists to join their teams — and that cyberthreats grow by the day while mitigation strategies do not — it is not possible that there are tens of thousands of professionals or experts in this field.
It’s no wonder why organizations of all sizes in a variety of industries are struggling to build a useful cyberthreat intelligence process. For companies that are just beginning their cyberthreat intelligence journey, it can be especially difficult to sort through all these moving parts. So where do they begin, and what can the cybersecurity industry do to adapt traditional threat intelligence models to the cyber battlefield?
How to Think About Thinking
A robust threat intelligence process serves as the basis for any cyberthreat intelligence program. Here is some practical advice to help organizations plan, build and execute their program:
Stop and think about the type(s) of cyberthreat intelligence data the organization needs to collect. For example, if a company manufactures athletic apparel for men and women, it is unnecessary to collect signals, geospatial data or human intelligence.
How much budget is available to collect the necessary cyberthreat intelligence? For example, does the organization have the budget to hire threat hunters and build a cyberthreat intelligence program uniquely its own? What about purchasing threat intelligence as a service? Perhaps the organization should hire threat hunters and purchase a threat intelligence platform for them to use? Each of these options has a very different cost model for short- and long-term costs.
Determine where cyberthreat intelligence data should be stored once it is obtained. Does the organization plan to build a database or data lake? Does it intend to store collected threat intelligence data in the cloud? If that is indeed the intention, pause here and reread step one. Cloud providers have very different ideas about who owns data, and who is ultimately responsible for securing that data. In addition, cloud providers have a wide range of security controls — from the very robust to a complete lack thereof.
How does the organization plan to use collected cyberthreat intelligence data? It can be used for strategic purposes, tactical purposes or both within an organization.
Does the organization intend to share any threat intelligence data with others? If yes, then you can take the old cybersecurity industry adage “trust but verify” and throw it out. The new industry adage should be “verify and then trust.” Never assume that an ally will always be an ally.
Does the organization have enough staff to spread the workload evenly, and does the organization plan to include other teams in the threat intelligence process? Organizations may find it very helpful to include other teams, either as strategic partners, such as vulnerability management, application security, infrastructure and networking, and risk management teams, or as tactical partners, such as red, blue and purple teams.
How Can We Adapt Threat Intelligence Models to the Cyber Battlefield?
As mentioned above, the threat intelligence models in use today were not designed for cyber warfare. They are typically linear models, loosely based on Carl Von Clausewitz’s military strategy and tailored for warfare on a physical battlefield. It’s time for the cyberthreat intelligence community to define a new model, perhaps one that is three-dimensional, nonlinear, rooted in elementary number theory and that applies vector calculus.
Much like game theory, The Diamond Model of Intrusion Analysis is sufficient if there are two players (the victim and the adversary), but it tends to fall apart if the adversary is motivated by anything other than sociopolitical or socioeconomic payoff, if there are three or more players (e.g., where collusion, cooperation and defection of classic game theory come into play), or if the adversary is artificially intelligent. In addition, The Diamond Model of Intrusion Analysis attempts to show a stochastic model diagram but none of the complex equations behind the model — probably because that was someone’s 300-page Ph.D. thesis in applied mathematics. This is not much help to the average reader or a newcomer to the threat intelligence field.
Nearly all models published thus far are focused on either external actors or insider threats, as though a threat actor must be one or the other. None of the widely accepted models account for, or include, physical security.
While there are many good articles about reducing alert fatigue in the security operations center (SOC), orchestrating security defenses, optimizing the SOC with behavioral analysis and so on, these articles assume that the reader knows what any of these things mean and what to do about any of it. A veteran in the cyberthreat intelligence field would have doubts that behavioral analysis and pattern recognition are magic bullets for automated threat hunting, for example, since there will always be threat actors that don’t fit the pattern and whose behavior is unpredictable. Those are two of the many reasons why the fields of forensic psychology and criminal profiling were created.
Furthermore, when it comes to the collection of threat intelligence, very few articles provide insight on what exactly constitutes “useful data,” how long to store it and which types of data analysis would provide the best insight.
It would be a good idea to get the major players in the cyberthreat intelligence sector together to develop at least one new model — but preferably more than one. It’s time for industry leaders to develop new ways of classifying threats and threat actors, share what has and has not worked for them, and build more boundary connections than the typical socioeconomic or sociopolitical ones. The sector could also benefit from looking ahead at what might happen if threat actors choose to augment their crimes with algorithms and AI.
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