Artificial Intelligence has become integral to protecting today’s complex networks. But effective security requires much more than mere technology adoption. 

The approach is just as important. 

Reactive security solutions use artificial intelligence (AI) to identify patterns and predict behaviours. But cybercriminals launch even more sophisticated AI-based attacks to overwhelm such solutions.

Enter Zero Trust as a solution to counter even such threats.

Zero Trust security assumes no device or user is inherently trustworthy. It enforces strict access controls and continuous authentication to eliminate the risks of any unauthorised traffic in the network.’

Zero Trust is an approach and not any specific set of tools. As such, success depends on implementation. An effective implementation requires the deep analytic capabilities and the deep intelligence of AI. 

The most common Zero Trust implementation methodologies include: 

• Access controls to restrict access only to authorised users.
• Micro segmentation, to block the malware’s pathways through the network.
• Tracking sensitive data and continuous monitoring, to block unauthorised attempts on such data.

Here are the top tools for effective zero-trust implementation, and how AI makes these tools more potent and effective.

Identity and Access Management (IAM) Tools

The core of zero trust is access controls. Identify and access management (IAM) tools enable granular access controls. It applies the principle of least privilege to give users access only to the resources they need.

IAM works through centralised identity management, multi-factor authentication, and role-based access control.

But getting IAM right requires a lot of work. And often, the task overwhelms security teams. The resultant gaps are a chief cause of security failure. 

Artificial Intelligence strengthens IAM by eliminating errors and gaps.

AI automates and streamlines IAM policy enforcement. Machine learning algorithms create and manage dynamic access policies. The algorithms consider user role, location, device type, and other real-time factors.  Changes in user status or role result in instant access rights changes. Manual changes take time, and threat actors may well exploit the time gap to launch their attacks. AI enables instance changes, eliminating such time-gap threats..

Network Access Control (NAC) Solutions

IAM allows authorised users access to the network and rejects unauthorised users. In a conventional network security approach, the device or user gets a free pass after that. 

But Zero Trust does not leave it at that. 

NAC solutions control network access on an ongoing basis. These tools regulate access based on device health, user identity, and policy compliance. Non-compliant devices face quarantine.

AI automate policy enforcement to make NAC tools more potent.

The algorithms 

• Automate user and device verification, authorisation, and policy enforcement. The process becomes friction-free and accurate.
• Identify patterns and trends in user behaviour to personalise security policies.

AI also enables proactive threat detection. AI’s continuous threat assessment capabilities allow adjusting policies based on real-time risks. Such dynamic policy enforcement applies only the necessary restrictions at any time, minimising disruption. The network also gets protection against the latest and emergent risks.

Network Segmentation Tools

Zero Trust Segmentation divides the network into smaller, isolated segments. Each segment gets relevant rules and policies on who can access it and what they can do within the segment. In the eventuality of a breach, the damage remains confined to the specific compromised segment. The different access controls and policies for other segments prevent lateral movement of the threat.

Microsegmentation also offers context into applications communicating with each other and the traffic flows between such applications. 

But the sheer size and complexity of today’s networks make segmentation a costly and time-consuming affair. AI tools make it easy to apply micro-segmentation consistently. The algorithms analyse complex network topologies to identify sensitive assets and applications. It generates granular policies for each segment that align with security best practices and organisational requirements.

AI also makes micro-segmentation dynamic. AI-powered tools leverage threat intelligence data and detect environmental changes to adjust policies in real-time. 

Cloud Access Security Brokers (CASB)

Cloud Access Security Brokers (CASBs) offer visibility and control over the network segments. Security teams use the tool to set cloud usage rules and then monitor cloud activity to ensure compliance with such rules. CASB tools can, for instance, apply control policies that restrict access to specific cloud resources based on user identity, device type, and location. On detection of suspicious activity, the tool shuts down the segment and/or revokes access.

AI-powered CASB tools:

• Classify sensitive data and apply appropriate protection measures better.
• Identify suspicious activities and trigger remedial actions such as blocking access. For instance, it looks for unusual login times, data exfiltration attempts, and unauthorised access. Correlating activities to normal behaviour patterns reduces false positives.
• Make dynamic policy adjustments. These tools can change security policies based on real-time risk assessments and shifts in the threat landscape. For example, if AI detects a new malware type, the CASB tool can changes access rules immediately to block its spread.

Cloudflare’s AI-powered Cloud Access Security Broker (CASB) scans applications for potential issues. It extends the automated checks to a wider area, such as if GitHub repositories have the correct membership controls.

Data Loss Prevention Tools

Effective Zero Trust depends on managing sensitive data from leaving the enterprise.

Data loss prevention tools classify sensitive data and block attempts to steal such data.

AI services allow enterprises to gatekeep access to sensitive content with a higher degree of perfection. AI-powered DLP tools manage approved services and block Shadow IT alternatives. These tools enforce authentication on every request made to the identified data. It also thwarts often-overlooked issues, such as a user inadvertently making files public.

Cloudflare’s Data Loss Prevention (DLP) tool protects sensitive data consistently across all users and devices, cutting across networks, SaaS applications, and other platforms. Administrators can build granular rules that distinguish shareable data from sensitive non-sharable data. Enterprises can create tokens to share sensitive data. Recipients can access the data only through these tokens. Teams can revoke tokens with a single click, and admins get automated logs of all access.

Zero Trust systems are not immune to the risk of human errors. Even without errors, human inefficiencies can blunt the effectiveness of these systems. Artificial intelligence reduces the risk of errors and makes the process much more efficient and potent. AI-powered platforms such as Cloudfare help enterprises leverage the power of AI while making their networks secure through Zero Trust.