Advancing Zero Trust Maturity: The Automation and Orchestration Pillar

Traditional security models focus their defenses on static, network-based perimeters, and operate under the assumption that everything inside the network is trustworthy. In contrast, Zero Trust is a cybersecurity framework that operates on the principle of "never trust, always verify." This paradigm requires strict verification for every user and device attempting to access network resources, regardless of whether they are inside or outside the network perimeter. By continuously validating access requests and enforcing granular access controls, this approach best minimizes the risk of data breaches in today's digital landscape.

To aid organizations in their migration towards Zero Trust, the Department of Defense (DoD) published a Zero Trust reference architecture. This white paper establishes a framework that provides conceptual guidance using seven architectural pillars that work together to provide a comprehensive and effective security model. These pillars are User, Device, Application & Workload, Data, Network & Environment, Automation & Orchestration, and Visibility & Analytics.

This month, the National Security Agency (NSA) published a Cybersecurity Information Sheet (CSI) that provides guidance for maturing the capabilities of the automation and orchestration pillar. The automation and orchestration pillar focuses on automating security operations and coordinating workflows to achieve faster and more effective threat detection and response. This approach reduces the reliance on manual processes, minimizes human error, and allows security teams to focus on strategic tasks.

In this blog post, we’ll delve into the key capabilities that define this pillar, which are:

• Policy Orchestration
• Critical Process Automation
• Artificial Intelligence (AI) and Machine Learning (ML)
• Security Orchestration, Automation, and Response (SOAR)
• Data Exchange Standardization
• Security Operations Coordination and Incident Response

Policy Orchestration

Policy orchestration in the Zero Trust framework involves the systematic management and implementation of security policies to ensure consistent enforcement across all environments. This process uses policy decision points (PDPs) to interpret security policies and determine access control decisions, which are then enforced by policy enforcement points (PEPs). Policies and the contextual data they depend on are stored in policy information points (PIPs).

Key aspects include:

• Policy Decision Points (PDPs): Evaluate access requests based on predefined policies and contextual data from PIPs.
• Policy Information Points (PIPs): Store policies and relevant contextual data.
• Policy Enforcement Points (PEPs): Implement the access decisions made by PDPs.

In traditional network environments, security devices often perform all these functions together. However, in a modern ZT environment, these functions are decoupled to allow for more flexible and dynamic security policies. For example, a network access request might be evaluated by a PDP, which consults PIPs for relevant policies and data before making an access decision that is enforced by a PEP.

Decoupling these functions enables dynamic and fine-grained access controls, which are essential for responding to changing security conditions. Policies in machine-readable formats can be automatically adjusted in response to evolving threats, improving the organization's security posture and responsiveness.

Maturity Levels

1. Preparation: Organizations use threat models, existing access requests, data flows, and capabilities in other ZT pillars as guidance for potential policies and policy-relevant metadata.
2. Basic: Establish a policy inventory and enterprise-wide access and security profiles. Collect and document existing rule-based policies for automation.
3. Intermediate: Establish PIPs and PDPs separate from PEPs to make data and service access determinations according to predefined policies.
4. Advanced: Ensure proper implementation of dynamic fine-grained data access policies for all access requests to resources.

Critical Process Automation

Critical Process Automation (CPA) within a Zero Trust framework involves automating an organization's crucial processes while adhering to the core principle that no entity, whether inside or outside the network, should be trusted by default.

Key aspects include:

• Robotic Process Automation (RPA): The automation of repetitive tasks and workflows - such as user provisioning, access request approvals, and security policy enforcement - to allow organizations to streamline processes and reduce human error.

• Integration of AI and Advanced Analytics:  AI and other advanced analytics can bolster an organization’s ZT posture by providing continuous risk assessment, behavioral analytics, predictive threat detection, and automated response.  These technologies help in identifying potential security threats more rapidly and enable earlier threat detection and response, thereby reducing the negative impacts of breaches and containing the damage.

 Maturity Levels

1. Preparation: Identify critical processes. Use mapping techniques to create diagrams of processes for better understanding and automation.
2. Basic: Apply integration and workflow provisioning at small scales, targeting critical processes first.
3. Intermediate: Expand automation using tools like RPA for repetitive tasks, optimize existing processes, and improve response times.
4. Advanced: Improve response times with orchestrated workflows and risk management processes. Nominate new processes for continual improvement.

Artificial Intelligence (AI) and Machine Learning (ML)

Artificial Intelligence (AI) and Machine Learning (ML) are integral components in advancing Zero Trust architectures. AI focuses on building systems that can perceive, reason, and act in ways that traditionally require human intelligence. This includes analyzing vast datasets, identifying security threats, and automating responses. AI's ability to rapidly process and analyze large volumes of data enables early threat detection and response, minimizing the impact of potential breaches.

Machine Learning (ML), a subset of AI, uses training data to develop models that predict, decide, and categorize new data inputs. ML models can be trained using supervised learning, which relies on labeled data, or unsupervised learning, which explores unlabeled data to uncover hidden patterns. Within ZT frameworks, ML can be used to achieve various security objectives, such as anomaly detection, user behavior analysis, and incident response.

ZT environments produce extensive data from access logs, network traffic, user behaviors, and security events. This data can train ML models to establish baselines and detect anomalous activities. When implemented correctly, ML solutions enhance User and Entity Behavior Analytics (UEBA) by identifying unusual user behaviors and supporting faster root cause analysis during security investigations. Integrating ML with network access controls and endpoint protection platforms further strengthens defenses against known and unforeseen threats.

Key Considerations include:

• Continuous human engagement and auditing: Crucial to ensure AI models function as intended, minimizing errors and preventing complacency.
• Regular training and awareness programs: Help employees understand the capabilities and limitations of AI within ZT architectures.
• Adherence to legal, regulatory, and privacy requirements: Essential when deploying AI and ML solutions.

AI Maturity Levels

1. Preparation: Define goals and use cases for AI implementation. Assess data for accuracy and relevance.
2. Basic: Obtain or develop AI tools based on use cases. Test and evaluate AI models for performance and accuracy.
3. Intermediate: Implement AI/ML tools driven by analytics and expand across the network. Address biases in models.
4. Advanced: Improve response times and capabilities with AI orchestrated workflows and greater automation of risk management processes. Automate ZT capabilities for prediction, anomaly detection, and response actions .

ML Maturity Levels

1. Preparation: Identify data sources and ensure data tags are standardized.
2. Basic: Implement data tagging and classification ML tools.
3. Intermediate: Employ ML tools for critical functions such as incident response and anomaly detection. Analyze and address model biases.
4. Advanced: Expand ML tools across the network, evaluate model performance, and optimize through hyperparameter tuning. Models self-evaluate with new data for continual improvement.

Security Orchestration, Automation, and Response (SOAR)

Security Orchestration, Automation, and Response (SOAR) encompasses technologies that enable organizations to collect and react to security-related data. SOAR systems gather and enrich data, apply decision logic, and execute tasks supporting security policies. Augmented by AI and ML, SOAR can enhance adaptive cyber defense, allowing for rapid response to cybersecurity threats and improving the organization’s security posture.

Key Components include:

• Threat and Vulnerability Management: SOAR solutions help in identifying and managing threats and vulnerabilities, enhancing the overall security posture.
• Security Incident Response (IR): Automation of IR workflows reduces response times and human error, making incident handling more efficient and effective.
• Security Operations Automation: Automates routine security tasks, freeing up human resources for more complex issues.

Within the Zero Trust (ZT) framework, SOAR automates and orchestrates security operations by ingesting alert data and triggering automated response playbooks. It enhances cybersecurity defenses by focusing on incident detection and response, fostering security team collaboration, and mitigating security threats swiftly and at scale.

Maturity Levels

1. Preparation: Develop a logging and audit policy for SOAR decisions and actions.
2. Basic: Acquire SOAR tools and implement policies and predefined playbooks.
3. Intermediate: Achieve initial operating capability of security technologies to automate policies and rule sets.
4. Advanced: Refine SOAR tools to improve security operations, threat management, and responses using alert data and playbooks for automated response and remediation.

Data Exchange Standardization

Data exchange standardization focuses on the uniformity of data formats, protocols, and application programming interfaces (APIs) to enhance communication, orchestration, and interoperability between services and applications. This standardization is critical for organizations relying on integrated and tightly coupled elements across their systems. By adopting common data formats and communication standards, organizations can seamlessly integrate diverse technologies into their security operations.

Maturity Levels

1. Preparation: Take inventory of processes, applications, workloads, and systems, especially integration points.
2. Basic: Research industry-adopted APIs and choose standards. Create a comprehensive catalog and style guide for APIs.
3. Intermediate: Standardize remaining APIs across projects, expand practices, and conduct function testing.
4. Advanced: Implement automated monitoring solutions to track performance and detect anomalies in APIs, protocols, and formats.

Security Operations Coordination and Incident Response

Security operations coordination and incident response are crucial aspects of an organization's cybersecurity strategy. Their main functions include detecting, responding to, and mitigating security risks, threats, and intrusions. Security operations centers (SOCs) play a central role by providing visibility into the security status and tactical implementation of the security operations. SOCs leverage automation tools and enrichments provided by service providers and technologies to streamline their workflows.

Key Components include:

• Security Operations Centers (SOCs): SOCs improve response times through rapid analysis and automated collection of relevant data, which is essential given the volume of data they manage.
  
• Incident Response Planning:  Robust incident response plans are critical in minimizing the damage from security incidents and ensuring the continuity of operations.
  

Maturity Levels

1. Preparation: Determine scope and objectives for SOC/IR teams.
2. Basic: Develop initial incident response plans and acquire necessary solutions.
3. Intermediate: Integrate SIEM solutions with data sources and begin initial monitoring and alerting.
4. Advanced: Provide advanced incident response workflow automation leveraging threat intelligence, UAM, and AI-based anomaly detection. Fully automate playbooks and leverage historical data in decision making.

Conclusion

In summary, for Zero Trust automation and orchestration, three key areas are crucial:

1. Automate Routine Tasks: Organizations should leverage automation to handle repetitive and predictable tasks related to critical functions like data enrichment, security controls, and incident response workflows. This automation should be coordinated across various systems to improve efficiency and reduce the manual effort required from security teams.

2. Utilize Advanced Technologies: Advanced algorithms and analytics, including AI and machine learning (ML), should be used to enhance key functions such as risk assessment, access management, environmental analysis, incident response, anomaly detection, user behavior baselining, and data tagging.

3. Enhance Security Operations: The effectiveness of a Security Operations Center (SOC) can be significantly improved by integrating AI/ML and automation. These technologies help in faster and more effective threat detection, response, and mitigation.

Schedule some time to talk about how you can use automation and orchestration to mature your organization's Zero Trust adherence.