Integrations and Data Collection

Data Management Challenges for Security Teams

In today’s rapidly evolving digital landscape, security teams face an ever-growing array of challenges. Among these, the integration of disparate systems and the collection of actionable data stand out as particularly daunting tasks. For CISOs and security leaders, especially within enterprises and mid-market teams, these issues are not just technical hurdles but strategic imperatives that can significantly impact the overall security posture of their organizations.

The Complexity of Integrations

One of the primary challenges for security teams is the integration of various security tools and systems. Modern security environments are often composed of a multitude of solutions, each designed to address specific aspects of security. These can include firewalls, intrusion detection systems, endpoint protection platforms, and more. While each tool may be effective in its own right, the lack of seamless integration between them can create significant blind spots.

Key Issues with Integrations

One of the primary challenges for security teams is the integration of various security tools and systems.

Variety and Complexity

Modern data landscapes are complex, hybrid, and multi-cloud and are composed of a multitude of tools for security, observability, and different types of storage. Building and maintaining the pipelines to manage these connections forces security teams to take on the burdensome role of data engineers, having to expend precious bandwidth to create regex patterns for inconsistent log formats, study API documentation to create integrations for different sources, and deal with vendor-specific data model requirements. Every new source being added is a new challenge and a drain on the time of security teams that could be spent on strategic initiatives to make their security infrastructure more secure.

Pipelines being secure

Earlier this year, customers of a major SaaS CRM application were informed that thousands of knowledge bases maintained by them on that application – by its very nature, business-sensitive, and worth securing – had been left vulnerable. The biggest culprit? Poorly configured access controls that were not conceived of or operated from a security-first perspective. Security is focused on risk-management and limiting exposure, while other business verticals and teams – managing observability, analytics, or data science – do not bring this perspective to bear.

The knowledge bases of the vulnerable application, for example, were susceptible to a large number of HTTP requests, and since the article IDs were numbered in a publicly-known, incremental discrete format, all a malicious actor needed to do was to repeatedly query the system across a finite set of possibilities until they chanced upon a knowledge base article left unsecured. A security-first perspective would ensure that such vulnerabilities were not left to be exposed by malicious actors, but a large number of data sources and integrations are not managed or administered by security teams, and instead add more effort in subsequent risk and threat detection as opposed to proactive security-first architecting.

At the time of writing this, the news is speaking of a data exposure rooted in configuration error in a public-facing developer hub for a major technology conglomerate with a big presence in cybersecurity. This clearly illustrates that as networks and systems become more extensive and attempts to capture and ransom sensitive data become more rampant, security teams need all the help they can get to manage these vulnerabilities better – and without errors or failure.

Interoperability Challenges

Different solution providers and vendors use proprietary protocols and formats, which complicate integrating their solutions. This increased complexity increases the time and effort involved in adding new sources. It also adds additional effort in maintenance, as changed formats or schema drifts can add to the engineering overhead that a security team has to deal with.

The Data Collection Dilemma

In addition to integration, security teams also have to contend with the challenges and complexities of collecting and ingesting data. Effective security monitoring and threat detection rely on the ability to gather data; this is why most security systems and tools rely on the aggregation of security events and logs, which is why security data collection continues to be linked to SIEMs for most security teams. However, this creates both an inescapable dependency and a singular point of failure, leaving security teams busy and overwhelmed.

Volume and Velocity

The sheer volume of data being generated by modern IT environments far exceeds the underlying design and systems built to manage them. This is why SIEM costs are rising manifold, and the number of false positives are creating headaches for modern SOC teams.

Data Quality

Not all data is created equal – and most of it requires context to be understood. Ensuring that all the relevant data is collected and that the irrelevant data is filtered out is essential for accurate threat detection and response. This requires extensive manual effort in cleansing data, and enriching it by contextually connecting it to other data from various sources to build a true and accurate picture of access and vulnerability. However, with the increasing volume and limited bandwidth of security teams, data quality issues can be missed or neglected, leading to missed threats.

Storage and Management

Storing and managing the large volumes of security data is costly and complex, particularly when compliance and audit requirements get involved. Enterprises often have to store security logs for a specific time period, creating vendor lock-ins. to deal with.

The Impact on Security Teams

Integration and data collection challenges profoundly impact security teams’ effectiveness. When systems are not properly integrated and data is not effectively collected and analyzed, security teams are left at a disadvantage. They may struggle to detect and respond to threats in a timely manner, leaving their organizations vulnerable to attack.

Consequences of Ineffective Integration and Data Collection

DELAYED THREAT DETECTION
Without a unified view of the security landscape, threats may go undetected for longer periods, increasing the potential for damage.

INCREASED WORKLOAD
Manual processes and fragmented systems can lead to increased workloads for securiry teams, contributing to burnout and reducing overall efficiency.

HIGHER COSTS
The lack of integration and efficient data collection can result in higher operational costs, as organizations may need to invest in additional tools and resources to compensate for these shortcomings.

The Need for a Holistic Approach

To address these challenges, CISOs and security leaders must adopt a holistic approach to security. This involves not only selecting the right tools but also ensuring that they can work together seamlessly. by prioritizing integration and data collection, security teams can enhance their ability to detect and respond to threats, ultimately improving their organization’s security posture.

Strategies for Improvement

What security teams need is a solution that makes integration and source addition a no-code, self-serve activity to reduce the effort involved. These systems should also help free data from format lock-ins so different tools and silos can speak easily with each other, to make data sharing and innovation easier. For this to happen – and for this system to be less error-prone or vulnerable – SOCs must look to AI-driven automation and solutions that can reduce the reliance on manual processes and free up security team bandwidth for strategic cybersecurity strategy.