The threat intelligence platform has emerged to become one of the most important weapons in the fight against cybercrime. From SMEs to the largest corporations and law enforcement to government agencies, threat intelligence platforms are playing an increasingly more significant role in making the world a safer place.
Among the many things threat intelligence platforms can do are data enrichment and risk scoring. The threat intelligence experts at DarkOwl explain enrichment and risk scoring as tools for transforming raw data into actionable insights while simultaneously prioritizing threats.
Enrichment: Exactly What Its Name Implies
Enrichment in a threat-intelligent environment does exactly what its name implies: it enriches raw data. For example, a threat intelligence platform can gather all sorts of raw data points, including:
- IP addresses
- Domains
- File hashes
Such raw data has limited value due to lack of contextual awareness. That’s where enrichment comes in. Enriching raw data involves combining it with historical data relating to known threats. Here are just a few examples:
- Geolocation Data – identifying the physical location of a suspect IP address can link equally suspect activity to a physical region. If that region is known for certain types of cyber threats, a better understanding of what could be happening is possible.
- Reputation Data – Raw data can be combined with reputation data to flag domains and IPs that have a history of nefarious activity, like phishing or command-and-control activity.
- Threat Actor Data – Threat actor TTPs can be combined with raw data to predict potentially adversarial behavior in the future.
Think of data enrichment as being similar to starting with a thesis statement and then building an entire argument around it. Raw data is like the thesis statement. It gives you a starting point. Enrichment expands on that raw data to provide context, history, and actionable insights.
Threat Intelligence Platforms and Risk Scoring
Enrichment is an excellent tool for providing context. But context alone can only go so far in preventing cyberattacks. Left simply with context, security teams can end up chasing low priority threats while ignoring their high priority counterparts. Overcoming that problem is the whole point of risk scoring.
In short, risk scoring is the practice of quantifying threat severity using specific parameters that can be modified as necessary. Risk scoring allows security teams to align risks with security posture, thereby giving them the tools to deal with the most severe risks first.
Risk scoring relies on three key factors:
- Source Reliability – All risk data is weighted based on the trustworthiness of its source.
- Corroboration – Risk data is scored higher if it is corroborated by multiple sources.
- Impact Potential – Scoring is higher commensurate with the potential impact of a given threat.
The best threat intelligence platforms are capable of making dynamic adjustments as needs arise. They make use of weighted scales and continual recalibration in order to account for emerging contexts. By making use of dynamic adjustments, threats can be tracked as they emerge and develop.
Enriched and Scored Data Makes the Difference
Organizations like DarkOwl utilize enrichment and risk scoring because it makes the difference between not responding to raw data and turning said data into insights security teams can act on. They do it because raw data alone isn’t enough to equip security teams with the knowledge they need to fight threat actors.
Organizations of all sizes and scopes already use threat intelligence platforms. Hopefully, those platforms make use of data enrichment and risk scoring. Otherwise, the benefits gained from threat intelligence are limited. It’s not enough to gather raw data. Security teams need to understand it too.
