Thanks to a combination of AI-led developments, socio-economic factors, and the sheer scale of innovations made possible, organizations' reliance on data will continue to expand. However, what makes this data so valuable for businesses is also what makes them such an attractive target of hackers and other malicious actors. Enterprises understand this well, thus explaining the immense R&D expenses being incurred for various data security, privacy, and governance measures. This ensures their data resources are kept secured while leveraging maximum value from them and enables proactive and timely regulatory compliance.
The Data Security Governance (DSG) Framework is one such process enterprises are increasingly embracing and adopting as a strategic means to mitigate and eliminate all potential risks to their data resources. Adopted properly, it empowers an organization with a clear set of policies, controls, and accountability structures that ensure all its data is appropriately secured, classified, and accessible only to authorized and relevant individuals and tools, consequently providing all such data resources with multiple layers of security.
Furthermore, it can be helpful in eliminating internal inconsistencies and fragmentation in data protection efforts throughout the organization through a uniform organizational culture and policy toward data security.
Read on to learn more about what makes the Data Security Governance Framework so important for organizations, the critical challenges enterprises face in implementing this framework, key elements involved in its deployment, and perhaps most importantly, how best to implement such a framework throughout the organization.
Why is a Data Security Governance Framework Essential?
The Data Security Governance Framework's importance for an enterprise can be assessed by looking at examples of what organizations have had to face in its absence.
Whether it's British Airways facing a $26 million fine for inadequate data security measures that affected 400,000 users, the cyberattack on TalkTalk where 100,000 customers were affected, leading to a fatal one-third reduction in the entire company's value, or the infamous AT&T data breach where thousands of customers' sensitive data ended up on the dark web, there is an entire litany of examples that should serve as ominous warnings for enterprises related to how important the Data Security Governance Framework has become.
At the same time, this should not give small businesses a false sense of security. Malicious actors are equally dangerous for them, with small businesses at the risk of losses of at least $50,000 per cyberattack. Furthermore, depending on another factor, such as the nature and scope of the incident, a business can suffer losses ranging from $25,000 to $200,000 from each cyber incident. This is just the financial repercussions, the regulatory consequences, legal actions, and the reputational loss, which can be fatal for enterprises that rely significantly on their customers' goodwill to remain operational.
Key Elements of an Effective Data Security Governance Framework
Some key elements involved in an effective data security governance framework include the following:
Risk Management Policies
Risk management is at the very core of a Data Security Governance Framework as it enables a proactive approach toward identifying, assessing, and mitigating security threats before they can escalate and pose greater problems. Per IBM's Cost of a Data Breach Report, organizations with an effective risk management policy adopted across the board experience lower breach costs, with financial damages being restricted by an average of $1.5 million per year per incident.
Through specific risk management practices, such as regular penetration testing, risk assessments, and continuous monitoring, organizations can ensure they remain fairly on top of the challenges they face. These can be further triangulated with internationally recognized frameworks such as the ISO 27005 (risk management in cybersecurity) or NIST 800-30 (risk assessment guidelines) to evaluate all manner of risks faced by an organization and establish relevant countermeasures.
Data Classification
Data classification can be immensely beneficial for two important reasons. It allows for better insights into the sensitivity of data resources and risk prioritization, as all protection measures can be classified based on the likelihood and potential impact of attacks on each asset. Such prioritization ensures a robust and efficient management of security budgets while enabling an organization to meet regulatory requirements for such classification.
The latter consideration can be of particular importance, considering how significant data protection regulations, such as the GDPR, HIPAA, Privacy Act, and others, strictly mandate different data security measures for various types of data, such as personally identifiable information (PII), financial records, and healthcare data. Such classification enables organizations to ensure all data resources that ought to be encrypted are encrypted, access to them is restricted, and additional security measures are in place to guarantee heightened protection.
Incident Response Planning
2024 saw a steep increase of almost 75% in cybersecurity incidents, a huge increase compared to 2023. Much like businesses, hackers and malicious actors are also leveraging GenAI capabilities as much as possible, making data breaches and other cybersecurity incidents increasingly inevitable. In such a cat-and-mouse scenario, these actors need only be lucky once, which can lead to losses in the millions of dollars for enterprises. Hence, it is equally vital for enterprises to have an incident response plan in place in case such an incident does occur.
With this plan, enterprises can detect, contain, and recover from any cybersecurity incident quickly and efficiently while eliminating any chances of prolonged damage from the incident. Furthermore, enterprises need not make such plans as static plan Bs through regular incident response drills. They can assess how their workforce and the plan are likely to perform and make adjustments accordingly to have a plan in place that minimizes any possible damages from the incident while also reducing the likely financial impact of the security incident.
Access Control Policies
Too many enterprises are so overtly focused on external threats that they may fail to adequately plan for possible insider threats. Insider threats are not always malicious. A single employee with improper password hygiene or a victim of a phishing attempt could lead to serious damage.
Access controls ensure data assets, especially sensitive data resources, are protected from any form of unauthorized internal or external access. These include a combination of zero-trust security architecture, role-based access controls (RBAC), and the principle of least privilege (PoLP) across the board to add the critical defensive layer to all data.
Moreover, such controls allow for greater visibility into how these data resources are being used across organizations, with user permissions easier to determine in a manner that does not hinder the organization's daily operations. Furthermore, they also allow for a chain of accountability in the event of a possible event.
Challenges in Implementing a Data Security Governance Framework
Some of the most immediate and critical challenges to resolve when implementing a data security governance framework include the following:
Lack Of Organizational Alignment
From a purely technical and operational perspective, the Data Security Governance Framework may be appropriate for meeting all the organization's challenges. However, one key roadblock that would undermine its effectiveness is the lack of leadership support and alignment across various departments. It is far too common for organizations to continue viewing data security as a purely IT responsibility rather than a holistic organizational consideration. Consequently, data security governance initiatives may struggle to garner adequate funding, resources, and the enthusiasm necessary to ensure their success.
Collaboration plays a vital role in ensuring the effectiveness of the Data Security Governance Framework. IT, legal, HR, marketing, and all other departments that deal with data in any considerable form must abandon their siloed approach to data security and adopt the unified alternative that a Data Security Governance Framework offers. Done properly, such a framework is a business enabler rather than just another compliance burden.
Evolving Regulatory Requirements
It may end up being one of the most pressing challenges when implementing a Data Security Governance Framework within the organization, i.e., the regulatory compliance requirements that necessitated the framework in the first place may have evolved by the time it is implemented. That is a factor organizations must consider since all major data privacy regulations consistently undergo major and minor amendments that modify the exact obligations upon organizations.
Automated compliance monitoring tools can be particularly important here. Manually attempting to stay on top of such an evolving regulatory landscape can be both resource-intensive and highly inefficient. A compliance-by-design approach where AI capabilities are leveraged in real-time risk assessments and compliance reports can enable all governance policies to be updated and amended as necessary for regulatory changes.
Resource Constraints
This is a simple reality for most enterprises. Budgetary considerations play a significant role in determining how best to leverage Data Security Governance Frameworks. For organizations that operate with tight budgets, limited expertise, and other more urgent business priorities, these governance frameworks can often end up on the back burner, even when there is general enthusiasm for their adoption.
Multiple costs are related to implementing a Data Security Governance Framework within an organization, such as risk assessment tools, compliance audits, dedicated security teams, and all other security measures. While each of these is effective in protecting data assets, they are far from inexpensive and represent a significant financial expenditure.
Integration Challenges
Modern enterprises operate within complex IT environments. As mentioned earlier, an organization must move away from the traditional siloed approach to data security, but that can often be easier said than done owing to a combination of legacy systems, multi-cloud infrastructure, third-party applications, and complex networks that differ from department to department. Hence, implementing a common Data Security Governance Framework can be both an operational and logistical challenge.
There may be incompatibility issues, a lack of interoperability, and an organization may deem the cost of adopting a particular governance initiative untenable owing to its incompatibility with its other systems. Failure to address these issues can lead to security loopholes and further inefficiencies that would undo the entire reason for considering Data Security Governance Frameworks in the first place.
Appropriate Identification Of Sensitive Data
Perhaps one of the more underlooked aspects of implementing a Data Security Governance Framework is the appropriate identification, classification, and cataloging of all data assets, including sensitive data. Most organizations lack an effective and structured approach to data discovery.
As a result, there is a critical gap in their understanding of their relevant obligations related to all such data, such as different regulatory requirements per the jurisdictions the data was collected. This can, in turn, lead to unnecessary exposure of critical information, inefficient compliance audits, misallocated data security resources, and make it more difficult than necessary to secure all data appropriately.
How Securiti Can Help
Enterprises must take into account multiple factors such as regulatory obligations, evolving market needs, and the emerging cyberthreats when devising the appropriate strategies and measures to secure their data assets, particularly ones of a sensitive nature.
Technical measures aside, effective data security can often come down to the operational processes an organization adopts, such as the data security governance framework. It not only ensures all the necessary processes, policies, and other considerations are in place per the organization’s needs, but is flexible enough to be updated and modified based on the enterprise’s evolving needs.
Securiti’s Data Command Center, a centralized platform that enables the safe use of data+AI with integration to Data Security Posture Management (DSPM) capabilities, is designed precisely to make the adoption of such a framework both easy and efficient.
Through its deep contextual data intelligence, access controls, and workflow orchestration across hybrid multi-clouds & SaaS, Securiti enables many world’s leading enterprises for data security, privacy, compliance, and governance initiatives
The Data Command Center comes equipped with several individual modules and solutions designed to ensure compliance with all major obligations organizations may be subject to. It can be of particular help with its dedicated access intelligence, data mapping, cataloging, and lineage solutions, among several others.
Furthermore, the user-friendly centralized dashboard provides real-time insights into an organization's obligations and compliance activities, enabling proactive interventions whenever necessary or convenient.
Request a demo now and learn more about how Securiti can help you comply with nearly all major data protection and privacy regulations from across the world.
FAQs about Data Security Governance Framework
Here are some of the most common questions you may have related to Data Security Governance Frameworks:
