Privileged Access

Azure Conditional Access provides organizations with a powerful tool to enforce security policies based on various factors, including user behavior, device compliance, and location. These policies can be configured through the Azure Active Directory (Azure AD) portal and are typically applied to cloud-based applications, SaaS platforms, and on-premises resources that are integrated with Azure AD.

To configure Conditional Access policies, administrators first define the conditions that trigger the policy, such as:

• User or group membership: Applying policies to specific users or groups within the organization.
• Sign-in risk: Assessing user sign-in risk levels, such as unfamiliar locations or suspicious behaviors, and enforcing additional controls like MFA.
• Device compliance: Ensuring only compliant devices (those managed through Intune or similar tools) can access organizational resources.
• Location: Restricting access based on trusted or untrusted IP addresses and geographic locations, blocking risky or suspicious login attempts.

Once conditions are set, administrators can then specify the actions to take, such as requiring MFA, blocking access, or allowing access only from compliant devices. For example, an organization could require MFA when accessing Microsoft 365 or other cloud applications from an unmanaged device or high-risk location.

Conditional Access policies are configured through the Azure AD portal and can be applied to a variety of platforms and services, including (but not limited to):

• Microsoft 365 (e.g., Exchange, SharePoint, Teams)
• Azure services (e.g., Azure Storage, Azure Virtual Machines)
• Third-party SaaS applications integrated with Azure AD

Multi-Factor Authentication (MFA) is a critical component of a comprehensive security strategy, providing an additional layer of defense by requiring more than just a password for system access. This multi-layered approach significantly reduces the risk of unauthorized access, especially in cases where an attacker has obtained or guessed a user’s credentials. MFA is particularly valuable in environments where attackers may have gained access to user credentials via phishing, data breaches, or social engineering.

For organizations, enabling MFA across all critical systems is essential. This includes systems such as Active Directory, VPNs, cloud platforms (e.g., AWS, Azure, Google Cloud), internal applications, and any resources that store sensitive data. MFA ensures that access control is not solely dependent on passwords, which are vulnerable to compromise. Systems that are protected by MFA require users to authenticate via at least two separate factors: something they know (e.g., a password), and something they have (e.g., a hardware token or a mobile device running an authenticator app).

The strength of MFA depends heavily on the factors chosen. Hardware-based authentication devices, such as FIDO2 or U2F security keys (e.g., YubiKey), offer a higher level of security because they are immune to phishing attacks. These keys use public-key cryptography, meaning that authentication tokens are never transmitted over the network, reducing the risk of interception. In contrast, software-based MFA solutions, like Google Authenticator or Microsoft Authenticator, generate one-time passcodes (OTPs) that are time-based and typically expire after a short window (e.g., 30 seconds). While software-based tokens offer a strong level of security, they can be vulnerable to device theft or compromise if not properly secured.

To maximize the effectiveness of MFA, organizations should integrate it with their Identity and Access Management (IAM) system. This ensures that MFA is uniformly enforced across all access points, including local and remote access, as well as access for third-party vendors or contractors. Through integration, organizations can enforce policies such as requiring MFA for privileged accounts (e.g., administrators), as these accounts represent high-value targets for attackers seeking to escalate privileges within the network.

It is equally important to implement adaptive authentication or risk-based MFA, where the system dynamically adjusts its security requirements based on factors such as user behavior, device trustworthiness, or geographic location. For example, if a subject logs in from an unusual location or device, the system can automatically prompt for an additional factor, further reducing the likelihood of unauthorized access.

Regular monitoring and auditing of MFA usage are also critical. Organizations should actively monitor for suspicious activity, such as failed authentication attempts or anomalous login patterns. Logs generated by the Authentication Service Providers (ASPs), such as those from Azure AD or Active Directory, should be reviewed regularly to identify signs of attempted MFA bypass, such as frequent failures or the use of backup codes. In addition, setting up alerts for any irregular MFA activity can provide immediate visibility into potential incidents.

Finally, when a subject no longer requires access, it is critical that MFA access is promptly revoked. This includes deactivating hardware security keys, unlinking software tokens, and ensuring that any backup codes or recovery methods are invalidated. Integration with the organization’s Lifecycle Management system is essential to automate the deactivation of MFA credentials during role changes or when an employee departs.

The Principle of Least Privilege should be enforced, and period reviews of permissions conducted to ensure that accounts have the minimum level of access required to complete duties as per their role.

An agent capable of Endpoint Detection and Response (EDR) is a software agent installed on organization endpoints (such as laptops and servers) that (at a minimum) records the Operating System, application, and network activity on an endpoint.

Typically EDR operates in an agent/server model, where agents automatically send logs to a server, where the server correlates those logs based on a rule set. This rule set is then used to surface potential security-related events, that can then be analyzed.

An EDR agent typically also has some form of remote shell capability, where a user of the EDR platform can gain a remote shell session on a target endpoint, for incident response purposes. An EDR agent will typically have the ability to remotely isolate an endpoint, where all network activity is blocked on the target endpoint (other than the network activity required for the EDR platform to operate).

An agent capable of User Activity Monitoring (UAM) is a software agent installed on organization endpoints (such as laptops); typically, User Activity Monitoring agents are only deployed on endpoints where a human user Is expected to conduct the activity.

The User Activity Monitoring agent will typically record Operating System, application, and network activity occurring on an endpoint, with a focus on activity that is or can be conducted by a human user. The purpose of this monitoring is to identify undesirable and/or malicious activity being conducted by a human user (in this context, an Insider Threat).

Typical User Activity Monitoring platforms operate in an agent/server model where activity logs are sent to a server for automatic correlation against a rule set. This rule set is used to surface activity that may represent Insider Threat related activity such as capturing screenshots, copying data, compressing files or installing risky software.

Other platforms providing related functionality are frequently referred to as User Behaviour Analytics (UBA) platforms.

An agent capable of User Behaviour Analytics (UBA) is a software agent installed on organizational endpoints (such as laptops). Typically, User Activity Monitoring agents are only deployed on endpoints where a human user is expected to conduct the activity.

The User Behaviour Analytics agent will typically record Operating System, application, and network activity occurring on an endpoint, focusing on activity that is or can be conducted by a human user. Typically, User Behaviour Analytics platforms operate in an agent/server model where activity logs are sent to a server for automatic analysis. In the case of User Behaviour Analytics, this analysis will typically be conducted against a baseline that has previously been established.

A User Behaviour Analytic platform will typically conduct a period of ‘baselining’ when the platform is first installed. This baselining period establishes the normal behavior parameters for an organization’s users, which are used to train a Machine Learning (ML) model. This ML model can then be later used to automatically identify activity that is predicted to be an anomaly, which is hoped to surface user behavior that is undesirable, risky, or malicious.

Other platforms providing related functionality are frequently referred to as User Activity Monitoring (UAM) platforms.

Audit Logs are records generated by systems and applications to document activities and changes within an environment. They provide an account of events, including user actions, system modifications, and access patterns.

A Data Loss Prevention (DLP) solution refers to policies, technologies, and controls that prevent the accidental and/or deliberate loss, misuse, or theft of data by members of an organization. Typically, DLP technology would take the form of a software agent installed on organization endpoints (such as laptops and servers).

Typical DLP technology will alert on the potential loss of data, or activity which might indicate the potential for data loss. A DLP technology may also provide automated responses to prevent data loss on a device.