如何利用人工智能增强API和微服务的云安全

译者 | 李睿

审校 | 重楼

概述

API 是一组指导方针和协议，用于促进软件应用程序之间的通信。它概述了软件组件交互的方法，简化了开发和集成系统的过程。

微服务架构代表了一种将大型应用程序划分为多个服务的软件开发方法。每个服务集中于一个业务功能。它们可以自主开发、部署和扩展。

云安全对API和微服务非常重要，其原因如下：

数据保护：API和微服务经常处理信息。维护安全性对于保护这些数据免受访问、泄漏或破坏至关重要。

用户身份验证和授权：在云计算环境中实现安全协议有助于验证和API和微服务交互的用户和服务的身份。这确保了授权实体可以使用服务。

（3）遵守法规：许多行业都有关于据隐私和安全的法规。遵守云安全实践可以确保符合这些要求。

确保安全通信：API和微服务依赖于网络通信。通过加密和其他保护措施保护这些通道，以防止数据拦截或篡改。这些通道对于防止数据拦截、截取或篡改至关重要。

（5）缓解DDoS攻击：有效的云安全策略可以帮助最大限度地减少分布式拒绝服务(DDoS)攻击的影响，这种攻击有可能破坏服务的可用性。通过防御攻击，API和微服务的可靠性得以维护。

（6）保护基础设施：在云计算环境中运行的API和微服务是基础设施的组成部分。安全措施在保护云计算基础设施免受可能被利用的漏洞侵害方面发挥着重要作用。

对于API和微服务来说，优先考虑云安全对于保护数据、确保合规性、维护服务可用性以及保护系统免受网络威胁至关重要。

云安全在没有人工智能帮助下面临的挑战

在没有人工智能帮助的情况下应对云安全挑战可能相当复杂。以下是一些典型的障碍：

（1）及时识别和应对不断变化的网络威胁对于维护安全至关重要。如果没有人工智能支持的威胁检测功能的帮助，组织可能会发现很难检测到可以逃避安全措施的威胁。

（2）人工实施安全程序耗时且容易出错。人工智能技术提供了自动化执行安全任务的能力，例如漏洞扫描、补丁管理和事件响应，使安全团队能够专注于战略工作。

（3）识别用户行为或网络流量模式中的异常对于确定安全漏洞至关重要。人工智能可以筛选大量数据，发现行为偏差，并发出警报以供进行进一步审查。

（4）考虑到云计算设置的性质不断变化，密切关注所有资产和操作可能会带来挑战。如果没有人工智能增强的监控工具的支持，快速发现和解决安全事件将成为一项相当艰巨的任务。

（5）云计算环境根据需要扩展，这使得确保跨所有实例的安全控制变得棘手。人工智能可以帮助自动化执行安全策略，并保证安全措施与基础设施一起可扩展。

人工智能增强API和微服务的安全性

人工智能增强的云安全解决方案在保护API和微服务免受新出现的网络威胁和漏洞方面发挥着作用。以下是人工智能增强API和微服务云安全的一些基本方法：

（1）检测和响应威胁：由人工智能增强的安全工具可以分析数据，以识别安全威胁模式，及时响应事件，防止数据泄露和未经授权的系统访问。

（2）发现异常：人工智能算法可以识别API请求和微服务活动中的行为，为活动、潜在攻击或未经授权的访问尝试发出警报。

（3）行为分析：人工智能对与API和微服务交互的用户和应用程序进行分析，识别正常的行为模式和可能表明安全风险的偏差。

（4）监控API流量：人工智能工具监控API流量，分析请求模式，并检测异常情况，以查明DDoS攻击、数据泄露或恶意活动等安全问题。

（5）安全策略的自动执行：人工智能系统自动执行API和微服务的安全策略，确保安全措施的应用，同时最大限度地降低错误风险。

（6）漏洞管理：人工智能增强的系统可以扫描API和微服务来识别漏洞，并根据风险评估确定修补的优先级。它们为加强安全以应对威胁和风险提供了建议。

（7）整合威胁情报：由人工智能增强的安全平台可以整合威胁情报，以跟上新出现的威胁、模式和攻击方法。这种集成有助于API和微服务的防御策略。

旨在加强网络安全防御、保护信息和维护基于云计算的应用程序安全的组织必须为API和微服务实施人工智能驱动的安全措施。通过利用人工智能技术，组织可以提高检测威胁的能力，减少事件响应时间，并在快速发展的数字环境中加强安全性。

1.检测和响应威胁

确保基于云计算的API和微服务的威胁检测和响应机制对于维护基础设施的安全性和可靠性至关重要。以下是在这种情况下如何利用人工智能进行威胁检测和响应的方法：

• 实时网络流量扫描：人工智能工具能够持续观察网络流量、API请求和微服务之间的交互，及时查明任何违规行为、可疑活动或未经授权的访问企图。
• 机器学习模型：利用人工智能增强的机器学习模型可以通过仔细检查数据集来检测建议采取行动的模式，从而识别网络威胁，主动识别和响应威胁。
• 自动事件响应：能够通过触发预定义的操作来响应安全事件，例如隔离受感染的服务阻止流量或隔离受影响的系统，从而自动执行事件响应过程。
• 取证分析：人工智能工具可以帮助在事件发生后进行分析，通过审查日志、审计跟踪和网络活动来重建事件序列，查明安全漏洞的原因，并指导补救工作。
• 自适应安全措施：人工智能有能力调整安全控制和设置，以应对不断变化的威胁，使基于云计算的API和微服务能够适应不断变化的风险和漏洞。

2.发现异常

异常检测在人工智能驱动的API和微服务中发挥作用，使系统能够识别与模式或行为的偏差。这个功能对于确保服务的可靠性、安全性和效率至关重要。

在人工智能增强的API和微服务领域，检测违规行为的算法可以用来监控请求/响应时间、流量、错误率和资源利用率等指标。通过及时检查这些指标，可以及时识别任何异常，促进干预和解决潜在问题。

将异常检测纳入到人工智能增强的API和微服务中，需要利用机器学习模型和算法来建立行为模式。任何偏离这些模式的情况都可能触发警报或自动响应，以解决风险或性能问题。

通过将不规则检测集成到人工智能增强的API和微服务中，组织可以提高系统的可靠性、安全性和有效性，最终提高用户体验和运营效率。

3.行为分析

当涉及到人工智能驱动的API和微服务时，分析用户行为包括监控和评估行为、趋势和偏好。通过观察用户如何与这些服务交互，组织可以发现不同的模式、异常和可以改进的领域。

该分析涵盖了用户参与程度、响应时间、错误频率和使用趋势等方面。在人工智能工具的帮助下，组织可以收集有关用户行为的信息，从而有效地定制他们的服务。

通过对人工智能增强的API和微服务进行分析，组织可以确定障碍、安全风险或需要性能升级的领域。通过掌握用户行为，企业可以改进其服务的可用性和响应性。

人工智能增强的API和微服务分析的整体集成可以提高用户满意度，提高系统效率，并根据数据驱动的见解做出明智的决策。

以下是一些在人工智能增强的API和微服务中通常用于分析行为的流行工具：

• Google Analytics: In addition to being used for web analytics, Google Analytics can also track user behavior in APIs and microservices, providing insights into user engagement, interaction, and conversion rates.
• Mixpanel: An analytics platform specializing in event tracking, Mixpanel allows organizations to monitor user behavior such as feature usage, retention and users across APIs and microservices path.
• Amplitude: Another tool focused on product analysis and behavior analysis, Amplitude Help organizations understand user behavior through cohort analysis and tracking user journeys for applications and services.
• Hotjar: Providing heatmaps, session recordings, and user feedback tools for analytics purposes, Hotjar visually demonstrates how users consume APIs and microservices.
• Kibana: Part of the Elastic Stack, Kibana is a data visualization tool for analyzing log data to identify user behavior patterns in APIs and microservices , anomalies and trends.

4. Monitor API traffic

Monitoring API traffic is essential to ensure manual The efficiency, security, and reliability of smart APIs and microservices are critical. By monitoring API traffic, organizations can monitor, analyze, and enhance data flows and requests within their systems. This monitoring helps pinpoint issues, anomalies, and patterns that may affect the normal operation of APIs and microservices.

# In an AI-enhanced environment, a variety of tools and methods can be used to monitor API traffic.

• Monitor API gateways: These gateways serve as entry points to APIs and provide insights into incoming and outgoing traffic. Tools like Kong, APIgee, or AWS API Gateway can monitor metrics such as request rate, response time, and error rate.
• Logging and tracing: Incorporating logging and tracing mechanisms in APIs and microservices can capture detailed information about API traffic.
• API Monitoring Tools: Because tools like Elasticsearch, Logstash and Kibana (ELK stack) are moving in the system, They are able to analyze logs and track requests. For monitoring APIs, you can use tools like Postman, Insomnia, or Paw to observe API endpoints, send trial requests, and measure response times. These tools have proven valuable for gaining insight into API behavior from a user's perspective.
• Real-time monitoring solutions: To enable real-time monitoring of APIs and microservices, solutions such as Prometheus, Grafana or Datadog provide features that enable organizations to Instantly visualize metrics, create alerts, and analyze performance in real-time.
• Traffic analysis tools: When it comes to analyzing network traffic and inspecting payloads in API communications, tools like Wireshark, Fiddler, or Charles Proxy can provide useful opinion.
• Security Monitoring Solutions: To enhance security measures around API traffic, security solutions such as OWASP ZAP, Burp Suite, or AWS WAF can be leveraged to effectively monitor security threats, anomalies, and vulnerabilities.

5. Automatic execution of security policies

Ensure the security of AI-driven APIs and microservices Security and confidentiality require automated enforcement of security policies. By automating this process, organizations can apply , monitor and enforce security measures consistently across their systems. Here are some specific strategies and tools that can be used to automate security policies in AI-driven APIs and microservices:

• Token-based authentication: Employ token-based authentication mechanisms such as OAuth 2.0 or JSON Web Tokens (JWT) to authenticate and authorize access to APIs and microservices; used to issue commands Automated process for acquiring and validating tokens. Revoking them helps increase security.
• Security policy as code: Use tools like Open Policy Agent (OPA) or HashiCorp Sentinel to define security policy as code. By converting security policies into code format, organizations can automate their implementation throughout the software development lifecycle.
• Behavioral analysis tools: Using analytical tools such as Splunk, Elastic Security, or IBM QRadar, you can monitor and evaluate user behavior to identify violations and maintain API and Security protocols for activities within microservices.
• Access Control List (ACL): Implementing access control lists helps limit access to API endpoints and microservices according to established regulations and ensures through automated management Only authorized users or services can access resources.
• Runtime Application Self-Protection (RASP): Use runtime (Runtime) application self-protection (RASP) solutions, such as Contrast Security or Veracode, involve embedding security policies into the API runtime environment to enforce measures against common threats such as injection attacks and data exposure.

6. Vulnerability Management

Effective vulnerability management is through identification, Assessing, mitigating, and monitoring vulnerabilities to protect systems from potential security risks and breaches plays an important role in protecting AI-driven APIs and microservices.

Here are some important strategies and tools for managing vulnerabilities in AI-powered APIs and microservices:

• Regular vulnerability scanning: Use scanning tools such as Nessus, Qualys or OpenVAS to check APIs and microservices for known vulnerabilities. Consistent scanning helps pinpoint weaknesses that attackers could exploit.
• Security patching: Establish a process for managing patches to ensure software components and dependencies are kept up to date with security patches. Timely patching can solve known vulnerabilities and reduce the risk of attacks.
• Secure Code Review: Regularly review code for security issues to address unsafe practices and vulnerabilities. Tools like SonarQube, Checkmarx or Fortify can automate code analysis to detect security flaws in your code base.
• API Security Practices: Follow API development practices including authentication, authorization, encryption, input validation, and error handling. Adhering to these practices can reduce the attack surface and enhance the security of APIs and microservices.
• #Integrate threat intelligence: Integrate threat intelligence to provide timely updates on emerging security threats and vulnerabilities related to AI-driven APIs and microservices. Tools like ThreatConnect or Recorded Future can help detect and respond to threats.
• Incident response plan: Develop a plan to respond to incidents involving API and microservice vulnerabilitiesHandling process. The plan should outline the process for handling a security incident, including steps to patch vulnerabilities, communications strategies, and recovery procedures.

7. Integrate threat intelligence

Integrate threat intelligence into artificial intelligence It is critical to implement intelligence-driven APIs and microservices to enforce security measures, identify threats and effectively manage risks. By leveraging threat intelligence resources, organizations can stay informed of threats, vulnerabilities, and malicious activity that may pose a risk to their APIs and microservices.

Here are some important strategies and tools for integrating threat intelligence in an AI-driven environment:

• Threat IntelligenceFeedback Sources: Stay current by subscribing to threat intelligence from trusted sources such as CERT, ISAC and commercial providers. These feeds provide real-time insights into known threats, indicators of compromise (IOCs), and attack patterns related to APIs and microservices.
Integrated Security Information and Event Management (SIEM): Integrate threat intelligence feeds into a SIEM solution such as Splunk, ArcSight or logrhym. This integration helps correlate security events, analyze logs, and identify threats based on indicators provided by threat intelligence.
Monitoring
• IntrusionsIndicators (IOCs): In logs of API and microservice traffic data and system activity , monitor monitoring Indicators of Compromise (IOC) matches from the threat intelligence feed. Tools like MISP (Malware Information Sharing Platform) or OpenCTI are valuable for managing and correlating monitoring indicators of compromise (IOCs). Enable Security Orchestration, Automation, and Response (SOAR): Deploy a SOAR platform such as Demisto, Phantom, or Swimlane to automate tasks related to managing threat intelligence. These platforms streamline incident response processes and remediation workflows designed specifically for APIs and microservices. Leverage machine learning algorithms to analyze data from threat intelligence, identify patterns, and predict security incidents in AI-driven APIs and microservices. Machine learning models can enhance the ability to detect threats and effectively automate response measures.
• Conclusion

Detect and respond to cloud-based APIs by integrating artificial intelligence-based technologies and threats in a microservices environment, organizations can strengthen their cybersecurity defenses, improve incident response capabilities, and protect their assets from cyber threats.

Original title:

##Leveraging AI to Bolster Cloud Security for APIs and Microservices By Nilesh Charankar

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