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Incident Response

Defense-in-Depth has Failed Us. Now What?

Defense-in-depth. It’s a philosophy we’re all familiar with: layering defenses so that if one fails, another layer is there to stop the attack. Sounds like a great approach, and it has become standard practice for the vast majority. The problem is that, frankly, it has not worked. For years we have been bombarded with a slew of headlines about compromises and breaches.

Defense-in-depth. It’s a philosophy we’re all familiar with: layering defenses so that if one fails, another layer is there to stop the attack. Sounds like a great approach, and it has become standard practice for the vast majority. The problem is that, frankly, it has not worked. For years we have been bombarded with a slew of headlines about compromises and breaches. And the velocity is increasing. In spite of all its layers of protection, defense-in-depth has failed us.

Questions about Defense-in-Depth

Why the Failure

There are various reasons why defense-in-depth has failed, stemming from the fact that each layer of defense has been a point product – a disparate technology that has its own intelligence and works within its own silo. This results in three key challenges. First, silos make it extremely difficult to share that intelligence – between tools or even teams – in any real way. Second, management complexity grows exponentially as you add additional management consoles for an already stretched security team. Third, these silos of technology just create an obstacle course for the attacker. But as the adage goes, “every obstacle is an opportunity,” and attackers capitalize on that. They successfully navigate this obstacle course every day to accomplish their mission – whether it is to steal, disrupt or damage what’s not theirs. Over time adversaries have evolved and so too have the technologies to catch them. However, the architecture has not. So even if the obstacle course may be harder, it is still an obstacle course…

As companies layer new products and technologies, they now find themselves with 40+ security products and vendors in 40+ silos. And because these products aren’t integrated, each layer in the architecture creates its own logs and events, generating a massive amount of data and a massive management challenge. Where does all this data go? How can you keep up with this data overload? Recent ESG research finds that 42 percent of security professionals say their organization ignores a significant number of security alerts due to the volume and more than 30 percent say they ignore more than half! In most cases, it is the security operators within the Security Operations Center (SOC) that find themselves drowning in this data as they undertake the onerous task of manually correlating logs and events for investigations and other activities.

In search of a solution

In an attempt to overcome the data overload challenge, SIEMs emerged as a way to store all this data and aggregate and correlate logs and events. This has worked to an extent; however, even SIEMs have limitations – some technological and some economical. On the technology front, SIEMs can be complex and, with today’s volumes of data, can face scale challenges. On the economic front, it can be costly for a company to store everything in the SIEM and thus they pick and choose what to include and what to exclude.

The SIEM has been the tool of choice for SOCs and it has certainly helped, but the volume of data is so great that security operators still can’t keep up. They are now looking at ways to mine through the SIEM data to find threats and breaches. One use case is to apply threat data from an outside feed – commercial, industry, government, open source, etc. – directly to the SIEM. Using data on threats found “in the wild,” the goal is to see what indicators of compromise (IoCs) may be ‘hidden’ in the vast amounts of data. In theory, applying threat feeds directly to the SIEM should work and provide some relief, but in reality this approach creates new and additional challenges for multiple reasons:

1. Lack of Context. SIEMs can only apply limited (if any) context to logs and events. Context comes from correlating events and associated indicators from inside your environment with external data on indicators, adversaries and their methods.

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2. False Positives. Without context it is impossible to determine the “who, what, where, when, why and how” of an attack, in order to assess the relevance to your environment. As a result, SIEMs generate frequent false positives. Security operators end up wasting valuable resources and time chasing problems that don’t matter.

3. Questionable Relevance. Threat intelligence feeds only offer “global” risk scores based on the provider’s research and visibility, not within the context of their company’s specific environment. Security operators using these global scores find themselves chasing ghosts.

4. No Prioritization. Prioritization based on company-specific parameters is imperative for faster decision making that improves security posture. Intelligence priority must be calculated across many separate sources (both external and internal) and updated as more data and context comes into the system.

5. SIEM Architecture Limitations. As previously mentioned, SIEMs themselves are already overwhelmed by the vast volumes of logs and events defense-in-depth generates. Adding millions and millions of additional data does not scale in an affordable way. In addition, SIEMs were built as a reactive technology to gather logs and events that previously occurred.  Aggregating threat data and intelligence to correlate, contextualize and prioritize in a proactive manner is not a SIEM’s primary design.

The result? Indicators of compromise are missed, scarce resources are squandered and attacks still succeed. 

Turning obstacles into opportunities

SOCs need to take a page from attackers and successfully navigate this obstacle course.

By automatically applying context, relevance and prioritization to threat data prior to applying it to the SIEM, the SIEM becomes more efficient and effective. Customized threat intelligence scores based on parameters you set, coupled with context, allows for prioritization based on what’s relevant to your specific environment. Now, using a subset of threat data that has been curated into threat intelligence, the additional overlay allows the SIEM to generate fewer false positives and encounter fewer scalability issues.

In addition, companies can make their entire security infrastructure more effective by using this threat intelligence as the glue to integrate layers of point products within a defense-in-depth strategy. By compensating for a lack of information sharing and providing richer insights, this approach helps SOCs to accelerate threat detection and response, and enhance preventative technologies with protection against future threats.

With less noise and streamlined operations SOCs can turn obstacles into opportunities. Instead of drowning in data they can prioritize their investigations on the highest risk threats first, stop attackers from successfully navigating the obstacle course and improve security posture.

Written By

Marc Solomon is Chief Marketing Officer at ThreatQuotient. He has a strong track record driving growth and building teams for fast growing security companies, resulting in several successful liquidity events. Prior to ThreatQuotient he served as VP of Security Marketing for Cisco following its $2.7 billion acquisition of Sourcefire. While at Sourcefire, Marc served as CMO and SVP of Products. He has also held leadership positions at Fiberlink MaaS360 (acquired by IBM), McAfee (acquired by Intel), Everdream (acquired by Dell), Deloitte Consulting and HP. Marc also serves as an Advisor to a number of technology companies.

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