Margaret’s post — est. reading time: 11 min

As artificial intelligence (AI) and machine learning (ML) move from experimental projects to production-grade services, they bring with them not only transformative capabilities but also new and often poorly understood security risks. While most DevSecOps pipelines are built to manage traditional application vulnerabilities, they are not always equipped to address the dynamic and data-centric risks introduced by AI systems.

From adversarial inputs and model poisoning to training data manipulation and privacy violations, the security profile of AI-enabled systems is both unique and evolving. Securing these systems requires a strategic rethinking of how models are trained, deployed, and monitored—integrating novel controls alongside traditional software security practices.

Why AI Security Demands Special Attention

AI systems differ fundamentally from conventional applications. Whereas traditional software follows predictable logic defined by developers, AI models learn behaviour from training data and improve through exposure to new inputs. This makes them both powerful and fragile. An AI model that performs flawlessly in the lab may behave unpredictably in the real world if presented with unexpected data patterns or malicious inputs.

Moreover, AI security risks are not limited to software vulnerabilities—they encompass the entire model lifecycle. Attackers may seek to poison training data, exploit inference logic, manipulate model updates, or extract sensitive data through queries. As organisations increasingly deploy AI in high-stakes contexts—from healthcare and finance to autonomous systems—the need for robust AI-specific security becomes critical.

Case Study: A Healthcare AI Gone Wrong

Consider the case of a healthcare analytics provider that deployed a predictive model to assess patient risk based on historical medical data. The system was designed to assist clinicians in early detection of chronic conditions. However, attackers discovered an opportunity to tamper with the model’s training data by injecting false records through a poorly secured data ingestion pipeline.

The result was subtle but damaging: the model began to misclassify certain high-risk patients as low-risk, undermining its reliability and potentially leading to dangerous clinical decisions. Although the breach did not involve theft or data loss, it represented a compromise of system integrity—one of the most serious and least understood types of AI attack.

Executive Insight: Trust Requires More Than Accuracy

For senior leaders, it is tempting to view AI through the lens of performance and opportunity. But trust in AI systems must also be underpinned by verifiability, resilience, and control. It is no longer sufficient to evaluate models solely on their predictive accuracy. Organisations must now ask: How resilient is this model to adversarial inputs? How secure is the training data? Can we explain and audit decisions? Who is accountable if the model is compromised?

In the DevSecOps context, these questions require AI security to be embedded throughout the development pipeline, just as code scanning and penetration testing are. Without this integration, AI becomes a blind spot—powerful, opaque, and vulnerable.

Unique Threats in AI and ML Pipelines

AI systems face a range of threats that differ from traditional software vulnerabilities. These include:

• Adversarial Examples: Slightly manipulated inputs designed to fool the model into making incorrect decisions while appearing legitimate to humans.
• Model Poisoning: Insertion of corrupted data into training sets to distort model behaviour in a targeted or unpredictable manner.
• Inference Attacks: Attempts to reverse-engineer the model or extract sensitive data from its responses.
• Model Drift and Degradation: Over time, models may become less accurate due to changes in input data distribution or user behaviour—potentially opening new vulnerabilities.
• Bias and Fairness Failures: Unintentional discrimination due to imbalanced training data, which may result in reputational or legal consequences.

Each of these attack vectors presents a different risk profile, and mitigation requires specialised tooling, process adjustments, and new types of expertise.

Securing the AI Lifecycle in DevSecOps

To secure AI systems in DevSecOps environments, teams must consider every stage of the AI lifecycle—from data ingestion and model training to deployment and monitoring. Below are the key areas of action and the controls that can be integrated into CI/CD pipelines:

1. Protect Training Data Integrity

Data poisoning can compromise a model before it ever reaches production. To counter this, teams should:

• Implement strict validation and sanitisation of incoming training data
• Use version control and access controls for datasets
• Monitor for outliers or suspicious data patterns before training
• Segment sensitive or high-risk data sources with stricter controls

2. Secure the Model Training Environment

Training often requires powerful hardware and access to sensitive data. Teams must:

• Harden infrastructure and isolate training environments
• Ensure audit logs capture all actions during training cycles
• Use encryption to protect training artefacts and models at rest and in transit
• Apply least privilege access for all users and services

3. Validate and Test Models for Security

Before deployment, models should undergo rigorous testing—similar to pre-release QA in traditional development:

• Test for adversarial robustness using synthetic attacks
• Perform explainability analysis to identify anomalies in decision logic
• Benchmark accuracy across different population groups to detect bias
• Run static and dynamic analysis tools on model-serving code

4. Harden Model Deployment and APIs

AI models are often served through APIs that accept input and return predictions. These interfaces must be secured like any production API:

• Require authentication and authorisation for all API access
• Throttle requests and monitor usage patterns to detect abuse
• Deploy models with container security best practices
• Encrypt prediction logs and avoid storing sensitive inputs in logs

5. Monitor and Respond to Model Behaviour in Production

Just like application telemetry, model monitoring is essential to detect drift, misuse, or degradation:

• Track prediction accuracy over time
• Monitor for anomalous input or output patterns
• Set up alerts for confidence score deviations or response delays
• Version and rollback models where needed to contain failures

Advanced Controls: Data Privacy and Cryptographic Protection

In regulated environments, data used to train and test AI models must be protected not only for security but also for compliance. Techniques such as differential privacy and homomorphic encryption can help mitigate risk by enabling computation on encrypted data or anonymised datasets.

Though these techniques are still maturing, they represent essential tools for any organisation seeking to build secure, privacy-preserving AI solutions at scale.

Building Security Awareness Across AI Teams

One of the biggest challenges in securing AI systems is the separation between data scientists, software engineers, and security teams. Bridging this divide requires not only shared tools, but shared understanding. Security concerns must be built into the mindset of model developers—not just those maintaining pipelines.

• Offer security training tailored to data scientists and ML engineers
• Involve security professionals in early-stage model design discussions
• Establish security champions within AI and analytics teams

Executive Recommendations: Leadership in AI Security

Leadership must ensure that AI is subject to the same scrutiny and governance as any other digital initiative. This includes:

• Mandating secure model lifecycle management policies
• Embedding AI security in enterprise risk and compliance frameworks
• Funding research into adversarial resilience and testing frameworks
• Requiring transparency and explainability in high-risk AI decisions

Most importantly, executives should foster a culture in which security is seen as a catalyst for innovation—not a constraint.

A Final Reflection

As AI continues to shape our digital future, the threats it introduces must not be ignored or underestimated. DevSecOps provides a natural home for AI security—enabling organisations to build smarter, faster, and safer systems. But this requires a deliberate expansion of the DevSecOps mindset to include model integrity, data trust, and continuous AI assurance.

In AI-driven environments, security is not just about protecting software—it’s about preserving truth, accountability, and trust in the systems we rely on to make decisions. If your organisation is serious about scaling AI, it must be equally serious about securing it.