A Monday morning security alert flags unauthorized access attempts, security group misconfigurations, and AWS Identity and Access Management (IAM) policy violations. Your team needs answers fast.
Security teams are using Kiro and Amazon Q Developer to handle repetitive tasks—scanning resources, drafting policies, and researching Common Vulnerabilities and Exposures (CVEs)—so engineers can focus on risk decisions and complex scenarios that require human judgment, resulting in faster threat response and more consistent security coverage.
This post shows you five ways to use Kiro and Amazon Q Developer to strengthen your AWS security posture based on the AWS Well-Architected Framework Security Pillar. Each technique builds on a common foundation described after the tool overview below.
Amazon Web Services (AWS) gives customers choices when it comes to AI-assisted development and security automation. Whether you prefer Kiro’s agentic integrated development environment (IDE) experience or the deep integration of Amazon Q Developer into your existing AWS environment, both tools can help you implement the security practices described in this post. The right choice depends on your team’s workflow, and in many cases both tools are complementary and can be used together.
Kiro is an AI-powered, agentic, IDE designed by AWS for specification-driven development, combining natural language prompting with structured, intentional coding to generate, test, and deploy applications.
Amazon Q Developer is the generative AI assistant integrated into AWS development and cloud environments, designed to answer questions, generate code, troubleshoot issues, and automate operational tasks across AWS services.
For setup instructions and to learn more, see the Kiro documentation and Amazon Q Developer documentation.
1. Embed security best practices with persistent context
Providing AI assistants with the right context helps them produce more consistent and relevant results. Each of the five techniques in this post becomes significantly more powerful when your AI assistant already understands your organization’s security standards. Setting up persistent context first means every subsequent interaction builds on that foundation, and the results you get from triage, remediation, reviews, and policy development will better reflect your specific environment rather than generic best practices.
Without persistent context, you need to repeat the same security requirements in every prompt such as "enable encryption, use least privilege IAM settings, and enable logging," which leads to inconsistent results and missed controls. Amazon Q Developer IDE Plugin rules and Kiro steering files (CLI and IDE) solve exactly this problem: you can use them to codify your organization’s security standards so AI automatically builds secure infrastructure consistently, without requiring you to repeat requirements in every prompt. Both tools support this capability independently, so you can configure whichever fits your workflow, or use both together for coverage across your full development environment. The following steps show you how to get started with each.
For Amazon Q Developer:
- Create directory:
.amazonq/rules/ in your project root.
- Create file:
.amazonq/rules/security-standards.md.
- Paste your organization’s security standards in natural language (see “Example security standards context file” below).
For Kiro (steering files):
In Kiro, persistent context documents are called steering files. They give the agent ongoing awareness of your architecture decisions, coding standards, and security requirements across every interaction and every session.
- Create file:
security-standards.md in your project root.
- Reference it in prompts:
Using security-standards.md as context, create....
Pro tip: You can use Kiro itself to help you create steering files. Describe your security requirements in natural language and ask Kiro to generate a structured steering file for your review before saving and activating it. This means your AI assistant can help you build the very context it will later use, making the setup process faster and more thorough.
Example security standards context file:
# AWS Security Standards
## Identity and Access Management
- All IAM roles must use least privilege principles
- Require MFA for console access
- Enable IAM Access Analyzer for all accounts
- Rotate access keys every 90 days
- Use IAM roles for EC2 instances, never embed access keys
## Data Protection
- Enable encryption at rest for all storage services (S3, EBS, RDS)
- Use AWS KMS customer-managed keys for sensitive data
- Enable encryption in transit with TLS 1.2 minimum
- Implement S3 bucket policies denying unencrypted uploads
- Enable versioning and MFA delete for critical S3 buckets
## Infrastructure Protection
- Security groups must follow least privilege (no 0.0.0.0/0 on sensitive ports)
- Deploy resources in private subnets when possible
- Enable VPC Flow Logs for network monitoring
- Use AWS WAF for public-facing applications
- Implement Network ACLs as additional defense layer
## Detective Controls
- Enable CloudTrail in all regions with log file validation
- Configure CloudWatch alarms for security events
- Enable GuardDuty for threat detection
- Set up AWS Config rules for compliance monitoring
- Implement centralized logging with retention policies
## Incident Response
- Create SNS topics for security alerts
- Configure automated responses with AWS Lambda
- Maintain runbooks for common security incidents
- Enable AWS Systems Manager for secure instance access
- Implement automated backup and recovery procedure
What this unlocks:
Without persistent context, a prompt like Create a Lambda function to process customer data could produce a basic function with no encryption, logging, or IAM configuration. AI output is non-deterministic, meaning that without guidance it might or might not include those controls. Steering files and rules documents minimize those variables by providing stronger guidance as part of every prompt and inference input.
With your security standards embedded as in the example above, however, the same prompt generates a function with KMS-encrypted environment variables, a CloudWatch log group with 90-day retention, least-privilege IAM, VPC placement in private subnets, a dead-letter queue, and AWS X-Ray tracing—all automatically.
Where it works:
This persistent context approach applies across both tools and all infrastructure generation workflows:
- Amazon Q Developer IDE Plugin: Rules in
.amazonq/rules/ apply automatically to every code generation and review interaction.
- Kiro: Steering files provide the agent with continuous architectural and security awareness across sessions and projects.
The shift-left impact:
This approach isn’t a replacement for your existing continuous integration and delivery (CI/CD) security automation. It’s a powerful complement to it, and that distinction matters. By embedding security standards directly into the development workflow, you shift security validation further left than pipeline checks can reach. Developers across your organization, not just security specialists, can generate infrastructure that meets your security standards from the first line of code. This scales security expertise into non-security roles, empowers development teams to self-serve on compliance requirements, and reduces the volume of findings that ever reach your automated pipeline checks.
The result is security functioning as an enabler of faster development rather than a gate that slows it down, and security engineers spending their time on policy design and complex risk decisions rather than remediating avoidable misconfigurations.
Measurable impact:
Track these metrics to quantify the value of persistent context:
- Security findings during code review: Establish a 30–60 day baseline before enabling context files, then compare
- Time from development to deployment: Track average cycle time before and after
- Remediation cost: Research consistently shows defects fixed in development cost significantly less than those fixed in production. Track your own ratio for 60 days
- Standards consistency: Audit a random sample of infrastructure pull requests for compliance with your top 10 policies
Implementation recommendation: Start by codifying your top 10 most frequently violated security policies as context. Measure the reduction in these specific findings over 30–60 days to quantify the impact on your team.
2. Accelerate security finding triage and investigation
AWS Security Hub consolidates findings from services such as Amazon GuardDuty, AWS Config, Amazon Inspector, and third-party security tools into a single dashboard, providing centralized security finding visibility and built-in triage capabilities across your AWS environment. AWS Security Hub Extended will bring even more capabilities into this mix, giving customers expanded control and additional opportunities to leverage the AI-assisted workflows described in this post at greater scale and with deeper integration across your security toolchain.
Kiro can complement Security Hub by helping you correlate findings across accounts, understand CVE context, and develop remediation approaches, including:
- Query findings using natural language across multiple AWS accounts and AWS Regions
- Understand specific CVEs and their potential impact on your infrastructure
- Generate investigation queries for AWS CloudTrail and Amazon Virtual Private Cloud (Amazon VPC) Flow Logs
- Correlate security events across different time periods and services
- Access the latest AWS security documentation and best practices
How it works – Model Context Protocols:
To enable these capabilities, Kiro uses Model Context Protocols (MCPs)—a standardized way for AI assistants to securely connect with external tools, services, and data sources, enabling them to take actions, retrieve real-time information, and interact with APIs beyond their built-in capabilities.
Open source MCP servers for AWS are a suite of specialized MCP servers that enable Kiro to interact with AWS security services, providing real-time visibility into your security posture. To get started, configure security-focused MCP servers in your Kiro settings file (as shown in the following example). For full instructions on configuring MCP servers in Kiro, see the Kiro MCP documentation.
Note on authentication: Before querying Security Hub, verify you have configured valid AWS credentials for the target account. Set the AWS_PROFILE value to a named profile in your ~/.aws/credentials file that has the appropriate permissions, or configure credentials using the AWS Command Line Interface (AWS CLI) (aws configure). Without valid credentials for the target account, Kiro will not be able to retrieve findings.
{
"mcpServers": {
"awslabs.aws-api-mcp-server": {
"command": "uvx",
"args": ["awslabs.aws-api-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"AWS_PROFILE": "<PROFILE>",
"AWS_REGION": "us-east-1"
},
"timeout": 120000,
"disabled": false
},
"awslabs.cloudtrail-mcp-server": {
"command": "uvx",
"args": ["awslabs.cloudtrail-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"AWS_PROFILE": "<PROFILE>"
},
"disabled": false
},
"awslabs.iam-mcp-server": {
"command": "uvx",
"args": ["awslabs.iam-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"AWS_PROFILE": "<PROFILE>"
},
"disabled": false
}
}
}
Configure the following security-focused MCP servers to get started:
- AWS API MCP Server: Interact with Security Hub, GuardDuty, IAM Access Analyzer, and AWS Config through AWS CLI commands
- CloudTrail MCP Server: Audit trail analysis and compliance monitoring
- AWS IAM MCP Server: Manage IAM users, roles, policies, and permissions
- AWS Knowledge MCP Server: Access up-to-date AWS security documentation and best practices
For a full list of available MCP servers, see the awslabs/mcp GitHub repository.
Example: querying Security Hub with Kiro:
- Open your terminal or Kiro IDE.
- Enter the following command:
"Show me all critical security findings from Security Hub in my production account ID <ACCOUNT ID>"
Example output:
> Found 4 critical security findings in account 123456789012:
## Configuration Issues (2)
1. SSM.7 - SSM documents block public sharing disabled
- Status: FAILED (since July 17, 2025)
- Impact: Systems Manager documents can be publicly shared
- Remediation: Enable block public sharing setting for SSM documents
- [Documentation](https://docs.aws.amazon.com/console/securityhub/SSM.7/remediation)
2. Config.1 - AWS Config not properly configured
- Status: FAILED (since March 4, 2025)
- Issue: Config isn't recording required IAM resource types (User, Role, Group, Policy)
- Standards affected: CIS, NIST 800-53, PCI DSS, AWS Foundational Security
- Remediation: Enable recording for IAM resources in AWS Config
- [Documentation](https://docs.aws.amazon.com/console/securityhub/Config.1/remediation)
## Vulnerabilities (2)
3. CVE-2020-36242 in Lambda function: MyLambdaOne
- Package: cryptography version 3.2.1
- CVSS Score: 9.1 (Critical)
- Issue: Integer/buffer overflow in Fernet encryption class
- Fix: Upgrade to cryptography 3.3.2
- Function: SPC-1233HH5R-MyLambdaOne-lUh3ESH0MdXF
4. CVE-2020-36242 in Lambda function: AutoUpdateMyLambdaOne
- Package: cryptography version 3.2.1
- CVSS Score: 9.1 (Critical)
- Same vulnerability as above
- Function: SPC-1233HH5R-AutoUpdateMyLambdaOne-d9HIBfxThbFW
Real-world impact:
Security finding triage that previously required navigating multiple consoles, correlating logs manually, and researching CVE databases can be accelerated significantly. Teams that have integrated AI-assisted triage into their Security Hub workflows report reducing mean time to triage for critical findings from hours to minutes, enabling faster containment and more consistent coverage across accounts.
3. Accelerate remediation of security findings in your infrastructure as code
AI assistants can scan your infrastructure code and flag security issues with specific fix recommendations. However, implementing these changes requires careful review, testing, and validation before any changes reach production.
Important: AI-generated remediation suggestions must be reviewed by a qualified security engineer before implementation. Automated application of AI-generated changes without human validation can introduce unintended misconfigurations or service disruptions. Treat AI output as a starting point, not a finished product.
The workflow:
You can execute this workflow in either Kiro or Amazon Q Developer, depending on which tool fits your existing development environment:
- Ask Kiro or Amazon Q Developer to scan your infrastructure files and identify security gaps.
- Review AI-generated remediation suggestions with your security team.
- Test changes in non-production environments.
- Validate using AWS security services such as IAM Access Analyzer, AWS Config, and Security Hub.
- Deploy to production with monitoring and rollback procedures in place.
Example prompt:
"Scan my infrastructure at /path/to/templates, identify all S3 buckets without encryption, enable AES-256 encryption, add bucket policies to deny unencrypted uploads, and provide the deployment command"
What happens:
The AI assistant analyzes your infrastructure files, whether written in AWS CloudFormation, Terraform , or AWS Cloud Development Kit (AWS CDK), and identifies resources that violate security best practices. It then implements controls such as encryption at rest using AWS Key Management Service (AWS KMS) or Amazon Simple Storage Service (Amazon S3)-managed keys, adds bucket policies enforcing encryption in transit, configures public access blocks, and generates the exact deployment command with a change preview so you can review what will be modified before anything is applied.
Based on the example security standards context file above, the following controls would be applied across all generated infrastructure: encryption at rest and in transit, least-privilege IAM policies, security group optimizations, VPC configurations, logging enablement, and backup and recovery settings.
Validation required:
AI-generated configurations deserve the same thoughtful review as other infrastructure code. Even a policy that looks correct on the surface might need tuning to match your organization’s least-privilege standards, or encryption settings might need adjusting to satisfy specific compliance requirements. Running those changes through a non-production environment and having a human confirm the results before anything reaches production are part of good infrastructure practices, whether the code was written by a person or generated by AI.
Real-world impact:
Identifying non-compliant resources across multiple accounts manually can take many hours and generating remediation templates for each resource can add significant time. Security teams that have adopted AI-assisted infrastructure scanning report spending less time on manual identification and template generation, and with AI assistance the same identification and drafting work can be completed in much less time. Customers report that a full remediation cycle that previously occupied their team for the better part of a day can be completed in under an hour when AI handles the scanning and template generation. It is worth noting that manual remediation time grows considerably at scale, as remediating dozens of non-compliant resources is not a linear exercise. Validation time in non-production environments remains essential regardless of how the remediation was generated, and should always be factored into your planning.
4. Perform in-depth security reviews
Amazon Q Developer and Kiro can analyze your infrastructure code and identify potential security issues across multiple categories aligned with the AWS Well-Architected Framework Security Pillar.
Using Amazon Q Developer:
- Open your infrastructure file in your IDE.
- Select the code you want to review.
- Open the context menu and choose Send to Amazon Q, then choose Optimize.
- Select Focus on security best practices.
Using Kiro:
- Open your infrastructure file in Kiro.
- Enter a natural language prompt such as:
Perform a comprehensive security review of this CloudFormation template and identify all deviations from our standards.
- Kiro will automatically apply your steering files as additional context when generating its response.
- Review the findings and iterate with follow-up prompts.
Security categories evaluated: For the complete, up-to-date list of security categories and controls, see the AWS Well-Architected Framework Security Pillar documentation. Current categories include but are not limited to:
- Identity and access management: Overly permissive IAM policies, missing multi-factor authentication (MFA) requirements, unused credentials and access keys, cross-account access risks
- Detective controls: CloudTrail logging configuration, Amazon CloudWatch alarm coverage, GuardDuty enablement status, and AWS Config rule implementation
- Infrastructure protection: Security group misconfigurations, public subnet exposure, missing AWS WAF rules, unencrypted network traffic
- Data protection: Storage encryption status, KMS key rotation policies, backup configurations, S3 bucket access controls
- Incident response: Amazon Simple Notification Service (Amazon SNS) alerting setup, log retention policies, automated response mechanisms
Example output:
Security Recommendations:
- Enable S3 bucket encryption with KMS: Critical
- Implement least privilege IAM policies: High
- Enable GuardDuty threat detection: High
- Configure VPC Flow Logs: Medium
- Add WAF rules for API Gateway: Medium
- Enable CloudTrail in all regions: Critical
- Implement automated backup policies: High
Total security improvements: 23 findings across 5 Well-Architected pillars
Keeping your configuration files current:
A security architect review remains valuable for keeping your steering files and rules documents complete and current. The goal is an AI assistant that already understands your environment, not one that needs correcting after every interaction. Treat your configuration files as living documents and update them when your security standards evolve, when new services are adopted, or when post-incident reviews reveal gaps. As this post notes, project rules reduce architectural drift and help maintain consistency as AI agents operate more autonomously.
Real-world impact:
Security reviews that previously required a security engineer to manually inspect infrastructure templates line by line can be completed in significantly less time with AI assistance. Teams using AI-assisted security reviews as a pre-commit gate—before code reaches CI/CD pipeline checks—report catching a meaningful portion of security findings earlier in the development cycle where they are faster and less costly to address. Integrating this review step into pull request workflows means security validation happens continuously rather than only at deployment gates.
5. Assist with service control policy development
You can use AWS Organizations Service Control Policies (SCPs) to apply preventive controls consistently across every account in your organization, enforcing security baselines without relying on individual account administrators. Kiro can generate initial SCP drafts from natural language security requirements, speeding up the drafting and iteration process considerably. Because SCPs are preventive controls that can’t be bypassed by administrators, misconfigurations can cause organization-wide service disruptions, making expert validation and staged testing essential before any SCP reaches production.
Step 1: Generate an SCP draft:
Describe your security requirements in natural language:
"Create an SCP with these security controls:
- Deny creation of S3 buckets without encryption
- Require MFA for IAM user console access
- Prevent public RDS snapshots
- Deny security group rules allowing 0.0.0.0/0 on sensitive ports
- Enforce encryption for all EBS volumes
- Require VPC Flow Logs on all VPCs
- Deny IAM policy creation without approval tags
- Restrict resource creation to approved regions only"
Kiro generates a complete SCP policy JSON with proper deny statements, condition keys for MFA and encryption enforcement, resource-level restrictions, and regional compliance requirements.
Step 2: Validate and lint the SCP:
Use Kiro or Amazon Q Developer to assist with policy linting and initial testing as a first layer of validation. IAM Policy Autopilot, available as a Kiro Power with one-click installation directly from the Kiro IDE, can analyze your application’s usage and generate necessary permissions based on the SDK calls it discovers. IAM Policy Autopilot also integrates as an MCP server with Kiro, Amazon Q Developer, and other MCP-compatible coding assistants, making it a natural part of your existing workflow rather than a separate tool.
"Review this SCP JSON for syntax errors, overly broad deny statements, and missing condition keys. Flag any statements that could unintentionally block legitimate operations."
The IAM Policy Simulator then adds another layer of validation on top of the AI-assisted linting, so you can test policy behavior, verify condition keys are correctly applied, and confirm that no legitimate operations are unintentionally blocked. IAM Policy Autopilot complements existing IAM tools such as IAM Access Analyzer by providing functional policies as a starting point, which you can then validate using IAM Access Analyzer policy validation or refine over time with unused access analysis. Together, these tools form a layered validation approach where each one strengthens the output of the previous step.
Step 3: Test in a sandbox environment:
Create a test organizational unit (OU) with non-production accounts and apply the SCP to the test OU. Attempt operations that should be blocked and confirm that no legitimate operations are unintentionally blocked. Use Kiro to pre-validate your infrastructure code against the proposed SCP before sandbox testing:
"Analyze my current infrastructure against this proposed SCP and identify resources that would be non-compliant"
This scan covers your infrastructure code files. For live account scanning across your organization, use the following AWS services:
- AWS Config with the Config Aggregator and Conformance Packs for continuous compliance monitoring across your organization.
- IAM Access Analyzer for automated reasoning-based analysis of external access, internal access, and unused permissions.
- Account Assessment for AWS Organizations for bulk scanning of identity-based, resource-based, and service control policies across all accounts.
- Security Hub for centralized aggregation of compliance findings and security scores across your entire organization.
Step 4: Security architect review:
Engage your security architects to identify potential risks and verify the policy aligns with your security framework. Check for conflicts with existing SCPs by reviewing all SCPs attached to parent OUs and the root in the AWS Organizations console. Use the IAM Policy Simulator to test interactions between policies and verify that emergency access procedures ( SEC03-BP03 Establish emergency access process – Security Pillar and SEC10-BP05 Pre-provision access – Security Pillar) remain functional before any production rollout.
Step 5: Staged rollout:
Deploy to development accounts first and monitor for policy violations and operational issues. Gradually expand to additional environments and maintain documented rollback procedures throughout the process.
Important: It’s strongly recommended not to deploy AI-generated SCPs directly to production without thorough expert review and staged testing. A misconfigured SCP can cause organization-wide service disruptions affecting every account in your organization.
Real-world impact:
SCP drafting that previously required security architects to write and iterate on complex JSON policy documents manually, often spanning multiple review cycles over several days, can be condensed when AI handles the initial drafting and linting. Your architects can then focus their time on policy design, edge case analysis, and organizational impact assessment rather than JSON syntax and structure.
Responsible implementation framework
Adopting AI-assisted security workflows is most effective when introduced gradually, with clear validation gates at each stage. The following two-phase approach gives your team time to build confidence, measure results, and establish the internal practices needed before expanding to production environments.
- Phase 1: Development and testing (weeks 1–4): Start by testing AI-generated security controls in isolated development accounts. Validate functionality, identify edge cases, and deploy to a dedicated testing environment with thorough security validation. Use IAM Access Analyzer, AWS Config, and Security Hub to verify that generated controls behave as expected. This phase is also the right time to build internal expertise across both your security team and your development teams, so that knowledge of what works and what requires human review is shared broadly from the start.
- Phase 2: Staging and production (week 5 and later): Apply the validated controls to a staging environment that mirrors production. Conduct penetration testing where appropriate and validate that monitoring and alerting function correctly before expanding further. Gradually roll out to production accounts with continuous monitoring in place. Maintain rollback procedures throughout and establish feedback loops so that lessons learned in production flow back into your steering files, rules documents, and validation processes over time.
What distinguishes the approach in this post from general guidance on AI coding assistants is the specificity of the security integration. There’s no shortage of content about how AI assistants accelerate development. What this post focuses on is how to configure both Kiro and Amazon Q Developer to perform security-specific tasks: triaging findings from Security Hub, remediating infrastructure code vulnerabilities against your organization’s defined standards, conducting Well-Architected security reviews, drafting and validating SCPs, and generating secure-by-default infrastructure through persistent context that reflects your environment rather than generic defaults.
Kiro is an agentic IDE that helps you go from prototype to production with spec-driven development, and its steering files give the agent persistent awareness of your security standards across every session. Amazon Q Developer complements this by providing deep integration into your existing AWS environment and IDE workflows. Together, these tools extend your security team’s reach into every stage of the development lifecycle, scale security expertise into development teams, and reduce the gap between when vulnerabilities are introduced and when they are caught. As the AWS Well-Architected Framework Security Pillar establishes, embedding security early and consistently across the development process is foundational to a strong security posture.
These five techniques aren’t about replacing your security controls. They’re about making security a natural part of how your teams build on AWS, regardless of whether they’re security specialists or application developers. In addition to the five techniques covered in this post, the following AWS capabilities complement this approach and are worth exploring for a more complete picture:
- Amazon Inspector is a vulnerability management service that continually scans AWS workloads for software vulnerabilities, code vulnerabilities, and unintended network exposure. It automatically discovers and scans Amazon EC2 instances, container images in Amazon ECR, AWS Lambda functions, and first-party code repositories. Amazon Inspector integrates directly into CI/CD pipelines through plugins for Jenkins, TeamCity, GitHub Actions, and Amazon CodeCatalyst, which teams can use to catch vulnerabilities before deployment. Its code security capabilities include Static Application Security Testing (SAST), Software Composition Analysis (SCA), and infrastructure as code (IaC) scanning, with native integration to GitHub and GitLab. All findings are surfaced directly in Security Hub for centralized visibility and response across your organization.
- Amazon Q Developer security scanning provides real-time security issue detection in the IDE, including SAST scanning for security vulnerabilities, secrets detection, IaC security evaluation, and software composition analysis for third-party dependencies. These capabilities are available across JetBrains, Visual Studio Code, and Visual Studio.
- Kiro Powers are curated and pre-packaged MCP servers, steering files, and hooks validated by Kiro partners to accelerate specialized development and deployment use cases. Security-relevant Kiro Powers include the IAM Policy Autopilot Kiro Power for baseline IAM policy generation and the real-time coding security validation MCP server pattern for Kiro.
- AWS Security Agent is a frontier AI agent that proactively secures your applications throughout the development lifecycle. Security teams define organizational security requirements once in the AWS Security Agent console, such as approved encryption libraries, authentication frameworks, and logging standards, and AWS Security Agent then automatically validates these requirements throughout development by evaluating architectural documents and code against your defined standards. It provides three core capabilities: design security review for architecture documents, code security review that automatically analyzes pull requests against your defined standards across connected repositories, and on-demand penetration testing that discovers, validates, and reports vulnerabilities through sophisticated multi-step attack scenarios customized for each application. When vulnerabilities are found, AWS Security Agent creates pull requests with ready-to-implement fixes directly in your code repository. Customers report that AWS Security Agent compresses penetration testing timelines from weeks to hours, transforming penetration testing from a periodic bottleneck into an on-demand capability that reduces risk exposure and scales security reviews to match development velocity.
- AWS Security Hub automated response and remediation provides pre-built playbooks for common findings using AWS Systems Manager Automation, enabling your team to act on findings faster and more consistently.
If you’re new to AI-assisted security workflows, the following week-by-week approach gives your team a practical path forward without overextending before the foundation is in place.
- Weeks 1 and 2: Set up your persistent context files with your top 10 security policies as described in the foundational setup section above. Configure MCP servers in Kiro for Security Hub and CloudTrail access and verify that credentials are correctly configured for your target accounts.
- Weeks 3 and 4: Run your first AI-assisted security review on a non-production infrastructure template. Compare the findings against your last manual review to establish a baseline for measuring impact over time.
- Weeks 5 and 6: pilot AI-assisted SCP drafting for one new preventive control. Run the full validation workflow including AI-assisted linting, IAM Policy Autopilot, and the IAM Policy Simulator before any production application.
- From that point forward: Measure the metrics outlined in the foundational setup section, update your steering files and rules documents as your standards evolve, and share findings across your security team, development teams, and platform engineering teams. The knowledge of what works and what requires human judgment is valuable to everyone who touches infrastructure in your organization.
Kiro and Amazon Q Developer give security teams practical tools to accelerate threat response and maintain consistent security coverage by handling the tasks that consume the most time with the least strategic value: scanning for known misconfigurations, drafting policy JSON, researching CVEs, and generating secure infrastructure. These AI assistants are most effective when paired with security engineers, as they accelerate assessments and code generation while human review, policy design, and risk judgment remain essential throughout.
By implementing the five techniques outlined in this post, starting with embedding security best practices through persistent context and then applying that foundation to Security Hub finding triage, infrastructure code remediation, in-depth Well-Architected security reviews, and SCP development, your team can strengthen your AWS security posture while maintaining the standards your organization requires.
AWS services such as Security Hub, IAM Access Analyzer, AWS Config, and CloudTrail provide the foundation for these AI-assisted workflows, enabling centralized visibility and automated validation of security controls across your environment. Emergency access procedures should be established and validated before deploying any preventive controls such as SCPs, following the break-glass guidance in the AWS Well-Architected Security Pillar and the AWS Prescriptive Guidance for break-glass access.
Start small with non-production environments, establish clear validation processes, measure results, and gradually expand your use of AI assistants as your team builds expertise and confidence. The result is faster threat response, more consistent security coverage, and security engineers focused on complex decisions rather than repetitive tasks.
If you have feedback about this post, submit comments in the Comments section below
Login
