Beyond Automation Part 1: How ServiceNow AI and AWS Transform Enterprise Security

In a hybrid cloud ecosystem, aligning cloud infrastructure with automated security is mission-critical. Many enterprises deploy both Infrastructure-as-a-Service (IaaS) and Software-as-a-Service (SaaS) solutions but find challenges integrating them to take advantage of out-of-the-box functionality and scalability. This leads to significant customization that increases cost and maintenance resources. Over time,  these systems become over-engineered, complex, and unscalable.

By integrating Amazon Web Services (AWS) with ServiceNow’s powerful Artificial Intelligence (AI) Operation (AI Ops) and Security Operations (SecOps) capabilities, enterprises can achieve stronger security outcomes while being strategic in their consumption, scaling, and intelligent orchestration of AWS.

In Part 1 of this series, Coalfire will highlight how combining AWS and ServiceNow AI features can simulteanously enhance security and drive operational efficiency with proven use cases.  The three featured use cases below include lessons learned for the reader to consider in each implementation.

1. Mitigating cyber threats from misconfigured AWS resources: Automate detection, remediation, and reporting of misconfigured AWS Resources using ServiceNow

When integrated with AWS Config and Security Hub, ServiceNow AI Ops  uses machine learning to detect misconfigurations in near real-time and trigger remediation actions required to maintain cybersecurity standards. Examples include reporting unencrypted S3 buckets or identification of non-compliant security groups. ServiceNow’s Virtual Agent or Flow Designer can automatically trigger remediation actions using AWS Lambda or Systems Manager Automation to mitigate potential data exfiltration. 

Value Impact

• Implementation significantly reduces Mean Time To Repair (MTTR), a key metric for availability and maintenance efficiency, depending on complexity of the AWS environment
• Improves compliance posture for asset populations of misconfiguration-related controls (e.g., Center for Internet Security (CIS) AWS Foundations, Federal Risk and Authorization Management Program (FedRAMP), etc.)
• Reduces human hours required to address tickets through the intelligent scaling of AWS Config, Lambda, and Systems Manager
• Native ServiceNow analytics allow for user friendly dashboards to report on misconfigurations and remediation status across the enterprise

Challenges

• Insufficient IAM Permissions: If ServiceNow does not have the correct roles to read AWS Config or execute Lambda functions, automation will not be successful.
• Poor Rule Tuning: Overly broad or misconfigured AWS Config rules can lead to alert fatigue or false positives, undermining the value of automation.
• Workflow Logic Errors: Flaws in Flow Designer or remediation scripts (i.e. wrong instance IDs or region) could cause partial or unsuccessful remediations.
• Inconsistent Tagging and Resource Metadata: Inconsistent tagging makes it difficult for AI Ops or workflows to identify and act on resources contextually.

2. Reduce waste using AI assisted risk scoring: focus cybersecurity efforts on high-risk areas based on near real-time risk scoring of AWS resources

Assets ingested from AWS (e.g., EC2, RDS, IAM entities) are risk-scored in ServiceNow using AI SecOps models trained on past incident and vulnerability patterns. These scores drive automated control testing and escalation in workflows. For example, a user onboards new AWS assets into ServiceNow’s CMDB and through use of AI trained on incidents, ServiceNow assigns a risk score to each asset based on things like known vulnerabilities and exposure.

Value Impact

• Improves prioritization accuracy and reduces risk of “paralysis by analysis” by frontloading the risk against the asset, as opposed to the individual vulnerability
• Increases efficiency of remediation teams by enabling them to prioritize resources towards the highest-risk assets
• Promotes consistent tagging, metadata enrichment, and full asset inventory in AWS and decreasing asset drift

Challenges

• Lack of Quality Historical Data: AI models require comprehensive incident, vulnerability, or threat history for effective training. Incomplete or biased data produces inaccurate scoring.
• Overfitting Risk Models: ML models trained on one environment (e.g., dev vs prod, or one isolated full stack (IFS) vs another IFS) may not generalize well, leading to misclassification of risk. This is particularly common when the same product offerings are supported by different tech stacks along industry verticals (government vs financial vs healthcare, etc.).
• Data Ingestion Gaps: If asset feeds from AWS (EC2, IAM, RDS) are incomplete or delayed, risk scores will be based on outdated or partial data.

• Resistance to Automated Decisions: Stakeholders may not trust risk scores unless model outputs are explainable and transparent.

   

3. Proactively detect and respond to threats with AI insights: Leverage ServiceNow threat intelligence with AWS and external data sources

AWS findings (e.g., GuardDuty, Macie) are enriched in ServiceNow Threat Intelligence using AI models that correlate indicators of compromise (IoCs) from third-party feeds. ServiceNow AI classifies and groups similar alerts for faster triage and threat hunting. For example, if GuardDuty flags suspicious behavior in an AWS service, ServiceNow Threat intelligence correlates the indicator of compromise with third-party threat feeds, enriching the details of the threat on the ticket viewable by the assigned remediation team.

Value Impact

• Reduce false positives with AI based correlation of threats
• Shorten Detection-to-response window by integrating AWS GuardDuty, Macie, and Security Hub into SOC workflows in ServiceNow
• This creates a measurement to justify investment in additional native AWS threat detection capabilities; as more high-level threats are effectively neutralized, further insight into other AWS assets becomes prioritized

Challenges

• No Standardized Threat Format: AWS services and third-party feeds may use different schemas for IoCs, leading to correlation mismatches.
• Data Volume and Noise: AI may be overwhelmed by low-quality or high-volume alerts unless strong filters or weighting logic is applied.
• Limited AI Explainability: If AI clusters threats without clear logic, analysts may distrust the output or miss real threats.
• Latency in Data Flow: Delays in pulling AWS GuardDuty or Macie findings into ServiceNow reduce effectiveness of real-time fusion

Mitigation considerations for potential implementation challenges:

Specific mitigations and dependency considerations for implementation of each use case is customer dependent. However, the following broad mitigations are good ways to strategically consider architecting the proposed use cases. 

• Establish strong CI/CD pipelines for updates to templates, workflows, and AI models.
• Use sandbox environments to test integrations before deployment.
• Enable explainability and confidence scoring in AI-driven outputs. 

Finally, ensure stakeholder alignment and feedback loops across cloud, security, and compliance teams.

Final Thoughts

Integrating AWS with ServiceNow AI features is a force multiplier—not just for automating security workflows, but for fueling intentional AWS usage across your organization to enhance security and drive operational efficiency.  

By utilizing cloud-native features and implementing secure best practices the default path, organizations unlock an effective cycle: better security + faster provisioning = intelligent AWS adoption.