Zero-Trust Architecture and modern AI Cybersecurity

Zero-trust architecture is an application design approach rather than a specific technology. It segments different areas of the application (or the account, from a user perspective) and requests discrete user verification for each one of these separately.

ZTA does away with the (frankly, obsolete) notion that everything inside an organization’s network should be trusted, once a “big bang” login is validated.

This type of “cybersecurity by architecture” defense greatly minimizes the surface of attack for cyber criminals. Since they cannot break through the whole account, they must instead break each area individually, greatly lowering their odds of success, and impact.

Zero-trust architecture is based on key principles, that will structurally aim to reduce cyber risk “at root”:

1. Least Privilege Access: This principle ensures that users and devices are granted the minimum level of access necessary to perform their tasks. Never give the user more credentials than they need. This limits the potential damage in case of a security breach.


2. Microsegmentation: To protect sensitive data, zero-trust strategies involve dividing security perimeters into small zones to maintain separate access for separate parts of the network. If one segment is compromised, the others remain secure.


3. Multi-factor Authentication (MFA): Zero-trust enforces stronger authentication mechanisms, requiring multiple proofs of identity before granting access, which dramatically enhances security.


4. Continuous Monitoring and Validation: Ensuring that all systems are secure and any anomalies are addressed immediately. ZTA makes it also easier in crisis management, should a specific area be compromised, to shut down only this one and not the other which are still secure.

ZTA has myriad benefits and applications in cybersecurity, owing to its elegant and simple “cybersecurity by design” fundamental approach. It is also in this sense, hugely beneficial to scaling cybersecurity.

However, if ZTA can mitigate cyber criminality impact across the board, it is specifically relevant to a few use cases in cybersecurity: Breached accounts, account fraud and takeovers, as well as countering ransomware and malware.

ZTA Vs. Account takeovers

Zero-trust architecture is of chief relevance to the growing class of account fraud and account takeovers, who have grown gigantically in the last few years.

Cybercriminals can now routinely break traditional login methods (keyword + 2FA or MFA) through phishing, credential stuffing, session hijacking, or simply buying broken session tokens on the dark web for less than $10 each. 2FA is simply not unbreakable anymore, and that is a growing problem.

However, if the application follows ZTA principles, the cybercriminal will suddenly realize that breaking login is only the beginning… And his dream of looting the account (of data, $, etc.) suddenly turns to a nightmare as they realize they must now break each and every part of the application.

ZTA Vs. Insider Threats

ZTA is also uniquely suited to counter a growing, underrated spectrum of cyber attacks: Insider threats. Oftentimes the cybercriminal may be an internal staff, and we also need protection for that possibility.

Should an admin have all credentials across the board, including user accounts? Probably not, according to ZTA. ZTA requires strict identity verification for every person and device trying to access resources on a private network, regardless of within or outside the network.

This approach minimizes the attack surface of the breached account/application, and reduces the chance of an insider threat or a breach from compromised credentials.

AI plays a pivotal role in enhancing zero-trust architectures by providing advanced threat detection, response, and predictive analytics.

AI systems can analyze vast amounts of data to identify patterns that signify potential threats, automate responses, and adapt security measures dynamically.

AI computational power can generally speaking, provide much finer analysis of the threat profile faced by the organization. Machine Learning can map out quickly, the typical paths taken by cybercriminals, from what are of the application to which, and design effective counter measures.

AI-led behavioral biometrics also help at another level: applying an overall layer of subtle protection to the account. By monitoring the current user signals, and how they compare to the normal usage of the account, they can freeze an account almost immediately.

Example: AI-Driven Behavioral Analytics

Consider a scenario where an employee's credentials are stolen. The cyber criminal immediately changes the password to fully take over the account.

From this point on, two scenarios are possible in the current state of cybersecurity:

1. Your organization only uses a keyword / 2FA for authentication, and does not monitor account usage. In this case, damage to the user may be substantial as the cybercriminal will enjoy a free reign inside the account for long enough to deal catastrophic damage. Even if you manage to get the account deleted or restored in 2-3 business days, the cyber criminal will likely steal vast amounts of data they will resell, exposing you to long term higher cyber risk. They may also empty your accounts and inflict substantial direct damage. Depending on what they previously knew of your personal data, they might be able to involve you in large commercial or legal commitments. Overall, an extreme risk profile for users (financial, legal, psychological) and SaaS applications owners (legal, commercial, reputational).


2. Your organization uses keyword / 2FA + ZTA design and AI behavioral biometrics for account monitoring. In this case, ZTA principles ensure that the criminal will first of all, get “stuck” into the first area they managed to breach, containing the threat. Very often, the cybercriminal will give up / realize that this target application is way too hard, represents too much work. Time is also against them, breaches do tend to get identified in time, and they will often give up as a result.
   
   On a separate defensive line (AI-led behavioral biometrics) they will also most likely get stopped right there, at login, or about 0.5s into login. By analyzing many data points at login alone (IP, IP address, changes, new device login, etc.) behavioral biometrics can typically already identify suspicious users. Depending on your cybersecurity posture and what mitigation you associated with what risk level, the cyber criminal might be stopped instantly (account freeze - waiting for manual validation). If you had a more liberal posture, they may have time to try for a transaction to a specific area of the app. But here again they will reveal extra data in real time (keystroke speed, mouse speed, going to a critical app area immediately, etc.) which will also clash with that of the usual user. Likely the cybercriminal would get stopped there at the latest, before any financial damage, data breach or identity theft had any chance to happen.

Financial Sector Implementation

A prominent example of zero-trust implementation can be seen in the financial sector. JPMorgan Chase adopted a zero-trust security model to protect against both external and internal threats.

By implementing strict access controls and segmenting their network, they were able to significantly reduce their attack surface and improve their overall security posture.

Government Adoption

The U.S. Department of Defense (DoD) has also moved towards a zero-trust architecture, driven by the need to protect some of the most sensitive information in the world. Their strategy includes comprehensive identity verification, device security compliance, and an AI-driven analysis system that continually assesses the trustworthiness of each request.

While zero-trust architecture offers substantial security benefits, its implementation is not without challenges. These include the complexity of redesigning the network to fit a zero-trust model and the need for significant cultural change within organizations as trust assumptions are overturned.

As we move forward, the integration of AI in zero-trust architectures is expected to become more refined. Predictive AI models will likely play a larger role in preemptively identifying potential threats based on evolving patterns. Moreover, as IoT devices proliferate and the corporate perimeter extends to include remote work scenarios, zero-trust principles will become even more critical.
In conclusion, zero-trust architecture is not just a security trend but a necessary evolution in the face of modern cyber threats. Its holistic approach, combined with the power of AI, creates a robust framework capable of defending against the most sophisticated attacks. As organizations continue to navigate the complexities of digital transformation, embracing zero-trust will be vital in safeguarding their digital assets and maintaining trust with their stakeholders.