As public cloud providers continue to elevate their platforms’ default enterprise protection and compliance capabilities to close gaps in their portfolio or suites of in-house integrated security products, CISOs are increasingly looking to the use and integration of threat intelligence as the next differentiator within cloud security platforms.
Whether thinking in terms of proactive or retroactive security, the incorporation (and production) of timely and trusted threat intelligence has been a core tenant of information security strategy for multiple decades — and is finally undergoing its own transformation for the cloud.
What began as lists of shared intelligence covering infectious domains, phishing URLs, organized crime IP blocks, malware CRCs and site classifications, etc., has broadened and become much richer — encompassing inputs such as streaming telemetry and trained detection classifiers, through to contributing communities of detection signatures and incident response playbooks.
Cloud-native security suites from the major public cloud providers are striving to use threat intelligence in ways that have been elusive to traditional security product regimes. Although the cloud can, has and will continue to collect and make sense out of this growing sea of raw and semiprocessed threat intelligence, newer advances lie in the progression and application of actionable intelligence.
The elastic nature of public cloud obviously provides huge advancements in terms of handling “internet-scale” datasets — making short work of correlation between all the industry-standard intelligence feeds and lists as they are streamed. For example, identifying new phishing sites without any user being the first victim, by correlating streams of new domain name registrations (from domain registrars) with authoritative DNS queries (from global DNS providers), together with IP reputation lists, past link and malware detonation logs, and continuous search engine crawler logs, in near real time.
Although the cloud facilitates the speed in which correlation can be made and the degree of confidence placed in each intelligence nugget, differentiation lies in the ability to take action. CISOs have grown to expect the mechanics of enterprise security products to guarantee protection against known and previously reported threats. Going forward, those same CISOs anticipate cloud providers to differentiate their protection capabilities through their ability to turn “actionable” into “actioned” and, preferably, into “preemptively protected and remedied.”
Some of the more innovative ways in which “threat intelligence” is materializing and being transformed for cloud protection include:
• Fully integrated protection suites. In many ways the term “suite” has become archaic as the loose binding of vendor-branded and discrete threat-specific products has transformed into tightly coupled and interdependent protection engines that span the entire spectrum of both threats and user interaction — continually communicating and sharing metadata — to arrive at shared protection decisions through a collective intelligence platform.
• Conditional controls. Through an understanding of historical threat vectors, detailed attack sequencing and anomaly statistics, new cloud protection systems continually calculate the probability that an observed sequence of nonhostile user and machine interactions is potentially an attack and automatically direct actions across the protection platform to determine intent. As confidence of intent grows, the platform takes conditional and disruptive steps to thwart the attack without disrupting the ongoing workflow of the targeted user, application or system.
• Step back from threat normalization. Almost all traditional protection technologies and security management and reporting tools require threat data to be highly structured through normalization (i.e., enforcing a data structure typically restricted to the most common labeled attributes). By dropping the harsh confines of threat data normalization, richer context and conclusions can be drawn from the data — enabling deep learning systems to identify and classify new threats within the environments they may watch over.
• Multidimensional reputations. Blacklists and whitelists may have been the original reputational sources for threat determination, but the newest systems not only determine the relative reputational score of any potential device or connection, they may also predict the nature and timing of threat potential in the near future — preemptively enabling time-sensitive switching of context and protection actions.
• Threat actor asset tracking. Correlating between hundreds or thousands of continually updated datasets and combined with years of historical insight, new systems allow security analysts to track the digital assets of known threat actors in near real time — labeling dangerous corners of the internet and preemptively disarming crime sites.
With the immense pressure to move from detection to protection and into the realm of preemptive response, threat intelligence is fast becoming a differentiator for cloud operators — but one that doesn’t naturally fit previous sharing models — as they become built-in capabilities of the cloud protection platforms themselves.
As the mechanics of threat protection continue to be commoditized, higher value is being placed on standards such as timeliness of response and economics of disruption. In a compute world where each action can be viewed and each compute cycle is billed in fractions of a cent, CISOs are increasingly cognizant of the value deep integration of threat intelligence can bring to cloud protection platforms and bottom-line operational budgets.