modern privacy framework

Why Organizations Need to Adopt Modern Privacy Framework

Data is the heart of all online and offline processes. Think of data as the building blocks of intelligence that is sourced to predict upcoming challenges and opportunities.

With data residing and thriving across multiple arenas, the growing number of data privacy regulations make it imperative for organizations and data handlers to protect the very fabric of data giving us a competitive edge over others and honoring data subject rights.

As organizations migrate towards hyperscale multicloud environments, ensuring the safe migration and classification of data within the cloud architecture has never been more crucial. While organizations embrace cloud-based applications, individuals are increasingly becoming aware of their rights and more willing to exercise them.

What is a Legacy Privacy Framework?

As the name suggests, the Legacy privacy framework is an outdated privacy structure that needs to be overhauled to comply with modern-day data privacy regulations and respect the rights of data subjects.

Manual mediums of classifying data, identifying data to its rightful owners, ensuring security standards are in place, fulfilling data subject rights, among others, are not only time-consuming but an inefficient approach towards handling modern-day data challenges.

Legacy frameworks usually rely on manual processes and are a reactive approach towards handling complex data challenges. They depend on information from other departments to assess risk and often focus on the paper-based checklist of perceived privacy rather than delivering the solutions modern-day data need.

Other than being an outdated method, legacy privacy frameworks come with a set of limitations, risks, and liabilities that make it difficult for modern businesses to achieve privacy compliance in a timely, efficient, and effective manner.

The legacy privacy framework is riddled with errors due to excessive manual procedures, not scalable due to big data sets and unwarranted human intervention, rarely integrated with security standards put in place by IT professionals, and not open to assessments, making it instead a dangerous bet to let it go on within your organization.

What is a Modern Privacy Framework?

Modern privacy framework leverages robotic automation to safely harness the incredible power of data and the cloud to address data challenges typically associated with a legacy framework.

The framework provides better discovery, indexing, and the ability to extract various categories of data by giving privacy teams in-depth and clear visibility into data sets without having to ask the data owners each time.

Automating data processes such as data assessments and providing a portal-based service for handling everyday data needs expedite the entire process, which can be repeated multiple times as and when required.

By embracing the power of automated AI-powered data intelligence, privacy, and security tools, organizations can 2x their goals by having a deep understanding of integrated data sets, universal consent management, data flows, data discovery, and classification.

Modern privacy tools equip themselves with the help of machine learning/AI and generate extensive records of processing activities (RoPA) while ensuring data processing impact assessments (DPIAs) are undertaken periodically.

The tools also ensure the utmost privacy or data and give privacy teams a cohesive view of privacy risks and ways to comply with international data privacy laws to meet data protection goals quickly.

Comparison – Legacy Privacy Framework vs. Modern Privacy Framework

Here’s a brief comparison of legacy privacy framework versus modern privacy framework regarding data discovery, data classification, data subject rights, data mapping, data assessments, data breach response, data risk, data governance, security controls, and consent procedures of each framework.

Principle Legacy Privacy Framework Modern Privacy Framework
Data Discovery & Classification · Takes a lot of time

· Expensive procedure

· Inability to identify personally identifiable information

· Individual context is absent

· Data discovery is automated

· Decreased operational cost

· Ability to recognize personal information with accuracy

· Efficient system

Data Subject Rights · Long email conversations

· Time intensive

· Manual labor required

· Increased overhead costs

· Data subject rights are fulfilled automatically

· Reduced processing time

· Reduced effort

· Increased accuracy

Data Mapping and Assessments · Old records

· Requires time to map data

· High chances of exploits

· Discover data instantly

· Accurately identify personal information

· Reduced chances of exploits

Data Breach Response · Difficulty identifying impacted individuals

· Incorrect breach notifications

· Labor intensive

· Delated response

· Autonomous data breach response system

· Integrated regulatory intelligence

· Instant response

· Reduced violations

Data Risk & Governance · Clogged visibility into real-time threats

· Restricted insights

· Manual data risk assessments

· Manual remediation process

· Data risks are visible in real-time

· Decreased overhead costs

· Automation remediation process

Security Controls · Unclear visibility of data

· Several vulnerabilities

· Reduced data exposure risks

· Reduced reputational damage

Consent · No mechanism of obtaining consent

· Outdated records

· Automated consent

· Ability to opt-in, opt-out and withdraw consent

Benefits of a Modern Privacy Framework

The benefits of a modern-day privacy framework are endless and rewarding.

  • Data Discovery and Classification
    Using Artificial Intelligence and Machine Learning, an automated framework discovers data assets and sensitive information in structured and unstructured data systems across on-premises and multi-cloud environments.
  • Automated Data-Centric Architecture
    Data brings several opportunities and challenges. An automated data-centric architecture results in unique scalability by allowing organizations to process vast volumes of data.
  • Improved Accuracy
    The automated data-centric approach accurately identifies data and maps data to the rightful owner while reducing liability.
  • Decreases Operational Costs
    Automation significantly reduces personnel’s operational and overhead costs, saving valuable time and money.
  • Integrated Tools
    Employees can be easily trained to operate automated tools and handle more tasks through a wide range of integrated tools.
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