How threat intelligence can improve vulnerability management outcomes

How threat intelligence can improve vulnerability management outcomes

Chris Jacob, Global Vice President, Threat Intelligence Engineers, ThreatQuotient, says an ever-evolving threat landscape needs a structured and efficient risk-based process for managing vulnerabilities.

It might surprise you to know that more than 70 new vulnerabilities are published every day. And, despite their risk-reducing value in helping SOC teams address these, vulnerability management solutions have drawbacks.

Often, they only provide a snapshot of an organization’s vulnerabilities at a point in time. In fact, owing to their nature, vulnerabilities identified today may not exist tomorrow or they may appear and disappear intermittently – leaving security teams scrambling to understand not only what the risk is but how it affects them and where they should start first with any remediation.

Often vulnerability management solutions struggle to support SOC teams effectively, meaning they face an uphill battle with fragmented tools and data silos.

This creates major challenges around alert fatigue and overloaded SOC teams who, despite all the tools available to them, end up undertaking manual investigations to determine the best response.

The problems with vulnerability management are complex and wide ranging, from technology to policy and governance. With the modern enterprise evolving to become more technologically distributed and cloud-aligned, the challenge is becoming even more complex.

End-to-end visibility into an organization’s technology stack is becoming harder to achieve, with shadow-IT only exacerbating issues.

Limited resources result in cybersecurity maintenance tasks that are never completed.

Additionally, the scope and impact of software supply chain risk is only just starting to become properly understood by those outside the software development industry.

Unfortunately, those that are responsible for patching and fixing software vulnerabilities are rarely involved in the technology selection process, leading to a lack of learning and improvement in technology selection choices. Layer onto this the escalating compliance landscape and it is easy to see how overwhelming the task is.

It is simply impossible to patch and mitigate every software vulnerability present in an enterprise network.

Historically, organizations would prioritize mitigation based on limited and inward-facing data, such as server versus workstation, an employee’s role, asset criticality, vulnerability score and patch availability.

Despite this level of prioritization, patching remains a time-consuming task with limited effectiveness because it doesn’t consider knowledge of how that vulnerability is actively being exploited in the wild, and the risks associated by those adversaries leveraging it, to a company’s specific environment.

Most companies focus more on the consequences and severity of a vulnerability versus the likelihood they may be impacted – if you focus too much on severity and consequence, you may not see the complete picture.

CVSS scores, for example, focus mainly on severity, with global values for likelihood that are assumed valid for all organizations – a mistaken assumption.

Yes, a vulnerability may be critical and of highest severity, but this vulnerability is relevant to your own organization because of the threats that target it.

This is where custom likelihood comes in. Understanding your own likelihood is critical for prioritization and triage.

The modern enterprise has a new wealth of internal and external data to make more data-informed choices about actions to take and the threats to respond to.

While exposure is an important input into the risk equation, it only really has relevance once certain elements of the vulnerability lifecycle are hit.

For example: What is the cost for adversaries to develop exploitation tools for the vulnerability – or is it now available within the existing off-the-shelf attack tool sets?

This is one of the largest influencers of likelihood of it targeting the masses. Does exploitation of the vulnerability result in a situation that fits into the threat actor’s tools, techniques and procedures (TTP) sweet spot, meaning it’s easy for them to execute upon their objective?

These are elements that the enterprise has absolutely no control over but can get visibility into to get ahead of the response process if answers to any of these questions is ‘yes’.

Or they can be used as critical inputs into a decision process to stop current mitigation efforts and pivot to other issues that are potentially more likely to impact the organization.

This is where using Threat Intelligence in conjunction with established vulnerability management practices can help organizations identify, prioritize and remediate vulnerabilities that have a higher risk profile or have the potential to have a greater impact on an organization.

To aid practitioners in vulnerability triage, it is desirable to have a list of vulnerability identifiers, presented in a prioritized list for mitigation. With the likelihood of exploitation being a key multiplier within the risk equation, it’s critical to have accurate, up to date and verifiable information that can help the organization understand the details of likelihood.

By combining information available from a variety of public and private, internal and external sources, prioritization lists can be improved for greater accuracy.

Automated assessment and scoring of a vulnerability likelihood is only now possible across the many disparate data sources by consolidating data into a single record of truth about what is known about the vulnerability.

Once a vulnerability hits a threshold value, or key elements of context are identified, automated actions can be initiated.

This helps move vulnerability management from its legacy (must patch everything) approach to a business focused risk-based approach and allows security teams to become part of the business decision-making process.

Using Threat Intelligence from internal and external sources, vulnerability management teams can identify, validate and orchestrate the entire vulnerability management life cycle of key assets that are at risk – quickly addressing and remediating through automation and collaboration.

In today’s ever-evolving threat landscape organizations need to adopt a structured and efficient risk-based process for managing vulnerabilities to reduce the risk of security breaches and improve overall security posture.

A data-driven threat intelligence approach is essential in this regard, as it enables organizations to identify and prioritize vulnerabilities accurately, allocate resources effectively and automate processes with high fidelity data.

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