Global concerns about data security and unauthorized access are paving the way for more innovation in protecting sensitive information.
One such innovation is Private AI, in which IT teams train artificial intelligence algorithms on proprietary data to ensure that it remains secure and private. Next is Zero-Trust Network Access (ZTNA) which optimizes VPN security by integrating stricter access controls and continuous monitoring.
Additionally, advancements in data masking and tokenization are revolutionizing the way companies secure sensitive data. Let’s explore these emerging technologies to learn more about their impact on digital privacy.
Private AI Models
Private AI refers to training machine learning algorithms on data that remains within the organization. This ensures proprietary data is not used to benefit competitors or third parties.
Companies can implement Private AI in the following ways:
The first is the in-house development of private AI. IT managers hire experienced teams of data scientists, IT engineers, and software architects to construct and maintain private AI models internally. The in-house approach offers complete control over the development cycle but requires significant investment in resources.
Alternatively, IT teams can also use platform-based solutions that enable private AI algorithm training while safeguarding privacy. Custom platforms allow teams to leverage AI features without compromising data or sharing it with third-party vendors. Integrating these platforms helps companies save money on large in-house teams.
Private AI helps businesses comply with the latest data protection regulations, preventing sensitive company data from contributing to shared intelligence that could aid competitors.
Enhancing VPN Security with Zero Trust Network Access (ZTNA)
VPNs like the VPN extension for Chrome provide secure remote access. Zero Trust Network Access (ZTNA) enhances this security by implementing more stringent access controls, assuming that no user or device is inherently trustworthy.
This approach significantly reduces security risks through the following integrations:
Multi-factor authentication (MFA) ensures VPN logins are secure beyond just passwords. Device posture checks verify device security before granting VPN access, including antivirus updates and OS compliance.
A least privilege access model ensures users only receive access to specific applications and data for their roles, mitigating potential damage from security risks. Micro-segmentation divides a network into isolated segments, preventing attackers from moving laterally from devices to user access controls.
ZTNA integrations have stronger monitoring capabilities, while context-aware access control considers location, time of day, and the application being accessed.
Data Masking vs. Tokenization
Watch for more advancements in data masking and tokenization.
Data Masking replaces real data with an imitation to build and test environments without exposing sensitive data. Masking is irreversible; therefore, it’s only used for testing and demos.
Tokenization also replaces sensitive data, except the data is replaced with random tokens that have no relationship to the original data. Unlike masking, original data may be retrieved securely. Tokenization is ideal for live environments where authorized access is still required.
Take Data Privacy to the Next Level
Talk to your IT team about integrating the above advancements into your data security model. Continue to track the progress of private AI, ZTNA advancements in VPN security, and the evolving roles of data masking and tokenization.
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