Securing the Future: The Critical Role of the Secure Development Lifecycle (SDLC) in Artificial Intelligence
- Nox90 Engineering
- Apr 20
- 5 min read
Executive Summary
Artificial Intelligence (AI) systems are transforming critical sectors such as healthcare, finance, education, and transportation. However, the integration of AI into organizational frameworks has created new, sophisticated attack surfaces for adversaries. This report presents a detailed overview of the Secure Development Lifecycle (SDLC) for AI, highlighting exploitation tactics, targeted sectors, and threat actor campaigns on a global scale, with special focus on the United States, Europe, and Asia-Pacific—regions experiencing the highest AI adoption rates. Notable breaches, such as the Microsoft AI Research Data Leak, and advanced persistent threat (APT) campaigns led by actors like APT41 and Charming Kitten (APT35), underscore the urgent need for robust, AI-specific security strategies. This advisory provides a comprehensive analysis of technical risks, exploitation in the wild, affected products and components, as well as actionable mitigation guidance. For further information or tailored security consultations, contact info@nox90.com.
Technical Information
The Secure Development Lifecycle (SDLC) for AI adapts classical security engineering to the unique characteristics of machine learning (ML) and data-driven systems. Each phase of the AI SDLC introduces risks that adversaries have exploited, requiring organizations to adopt a holistic, defense-in-depth approach.
1. Planning and Requirement Analysis
Risk assessment must identify AI-specific threats, such as data poisoning, model inversion, PII exposure, and compliance gaps (GDPR, AI Act). Data privacy is paramount, as large and sensitive datasets are essential for AI training. Failing to map data flows and access controls can result in unintentional data leakage or regulatory violations.
2. Design and Development
Security-by-design principles are vital. Threat models must address adversarial machine learning vectors, including adversarial input manipulation (MITRE ATT&CK T1606), data poisoning (T1586), and the risk of model backdoors. Secure data pipelines, strong authentication, and data anonymization are essential. Examples such as the Microsoft AI Research Data Leak (2023), where 38TB of sensitive data was exposed via an open Azure Blob, highlight the consequences of insecure storage and pipeline misconfigurations (Wiz Research Blog).
3. Model Training and Validation
Training environments must be isolated and access-controlled. Secure ingestion and validation of datasets are necessary to prevent data poisoning attacks, where adversaries subtly manipulate training data to compromise model integrity. Model theft and model inversion attacks are facilitated by exposed endpoints or weak controls (MITRE ATT&CK T1649). Validation processes should include adversarial testing, bias audits, and regular reviews for drift and performance anomalies.
4. Deployment and Monitoring
Models must be deployed in secure, monitored environments. Model integrity should be enforced using digital signatures and hashing mechanisms. Monitoring must detect anomalous inference requests, data drift, and performance degradation, which may indicate adversarial attacks or model extraction attempts. Unauthorized access or unusual traffic patterns to inference endpoints are common indicators of compromise.
5. Maintenance and Incident Response
Ongoing patch management is critical for both model-serving infrastructure and the AI models themselves. Regular retraining with validated, clean data is essential to prevent the propagation of poisoned or biased models. Incident response plans must address scenarios such as model compromise, data leakage, and adversarial manipulation, ensuring rapid containment and recovery.
Exploitation in the Wild
Several recent incidents demonstrate the active exploitation of AI SDLC weaknesses:
The Microsoft AI Research Data Leak (2023) involved the accidental exposure of 38TB of sensitive data, including model training data, internal communications, and credentials, through a misconfigured Azure Blob storage. This incident underscores the necessity of robust access controls, vigilant configuration management, and regular audits of data pipelines.
In the healthcare sector, adversarial input attacks have compromised AI diagnostic tools, leading to misclassification and potential patient harm. Research published in Nature demonstrated that adversarial samples can manipulate AI-based medical imaging systems, bypassing conventional controls.
APT groups are adapting their tactics to target AI systems:
APT41 (Double Dragon): This China-linked group targets proprietary algorithms and intellectual property, with a focus on healthcare and finance, stealing sensitive AI models and training data (MITRE ATT&CK: APT41).
Charming Kitten (APT35): This Iranian group has conducted campaigns targeting AI researchers and development environments, using phishing and credential harvesting to access model development pipelines (MITRE ATT&CK: APT35).
Indicators of Compromise (IoCs)
The following IoCs should be monitored within AI environments:
Exposed cloud storage URLs (such as Azure Blobs) containing sensitive data, model files, or credentials.
Unusual API request patterns or high-volume traffic targeting model inference endpoints, indicative of model extraction or probing.
Unexpected or anomalous outputs from AI models, potentially signaling adversarial input or data poisoning.
Unauthorized access attempts or anomalous account activity within training pipelines or data repositories.
Potentially Affected Products
The SDLC for AI is a methodology and not a vulnerability specific to one product. However, any machine learning platform, AI model management system, cloud-based AI service, or data science pipeline is susceptible. Notable platforms include:
Microsoft Azure Machine Learning (all versions, especially when used with public or misconfigured storage).
Google AI Platform and Amazon SageMaker where data exposure or pipeline weaknesses can be introduced through user misconfiguration.
Custom AI/ML frameworks such as TensorFlow, PyTorch, and Keras are also at risk if best practices in data, model, and pipeline security are not followed.
No explicit CVEs or vendor advisories enumerate affected versions for the SDLC process, but any deployment that does not integrate AI-specific SDLC controls is vulnerable.
Mitigation Strategies
Encrypt all sensitive data and restrict access to model training data and AI models. Implement data loss prevention (DLP) in all pipelines.
Isolate training environments, validate data sources, and enforce strict access controls. Regularly vet supply chain components and software dependencies.
Test models against adversarial samples and update training pipelines with adversarial robustness techniques where feasible.
Digitally sign and hash all models. Verify model integrity before and after deployment to prevent tampering.
Continuously monitor for data/model drift, abnormal inference requests, and unauthorized access attempts.
Maintain incident response playbooks for model/data leakage, adversarial manipulation, and supply chain compromise.
References
Nox90 is here for you
Nox90 provides comprehensive solutions to help organizations implement a Secure Software Development Lifecycle (SSDLC) tailored for AI and application security. We assist customers in embedding security at every phase of AI development, from data governance and threat modeling to deployment, monitoring, and incident response. Our expertise spans risk assessment, adversarial robustness, cloud security, and incident management, ensuring that your AI systems are resilient against evolving threats.
If you have any questions about this advisory, wish to discuss sector-specific risks, or require tailored guidance on securing your AI projects, please contact us at info@nox90.com. Our experts are ready to support your journey toward secure, compliant, and trustworthy AI solutions.
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