AI security isn't just DevSecOps with a new name.

While AI models run as software on specialized hardware and connect to networks like traditional applications, standard security practices—IAM access control, open-source scanning, and runtime protections—only scratch the surface of securing AI systems. The AI security landscape is evolving rapidly, with the OWASP GenAI team working tirelessly to map out threats and best practices. But organizations can’t afford to wait for industry consensus. They need to adapt existing DevSecOps tools, processes, and expertise to cover AI-specific risks. This is where AI security architects play a crucial role.

Why AI Security is Different

🔹 AI software includes data: AI isn’t just software—it’s also deeply tied to the data it was trained on. Traditional SDLC must expand to AI Data Lifecycle (AIDLC), incorporating concerns like data poisoning, privacy risks, and adversarial manipulation. AI security spans MLOps, DataOps, and software security, requiring new frameworks beyond what DevSecOps currently covers.

🔹 Pre-trained models introduce unique risks: Training models from scratch is expensive, so many teams rely on pre-trained models from repositories like Hugging Face. But unlike open-source software, AI models lack comprehensive threat intelligence—there are no CVEs for backdoored weights or trojaned layers. Developers must perform due diligence to assess model integrity before using one from public repo such as HuggingFace.

🔹 Model weights and configurations can be attack vectors: AI models contain parameters and configurations that determine their behavior. Attackers can manipulate these components in ways that are harder to detect than traditional software and configuration modifications. Malware can be injected into variety of model artifacts, making static analysis alone insufficient.

🔹 AI supply chain security is broader: AI security isn’t just about securing the model parameters—it includes dependencies in the software stack, the libraries it calls, and the datasets used for training. Understanding AI’s role in a system requires deliberate scoping, planning, and new compliance considerations. While SBOMs are evolving into AI-specific ML BOMs, these standards are still maturing.

🔹 Deterministic vs. Probabilistic behavior: Traditional security threats focus on unauthorized data access, privilege escalation, and fraud. AI introduces new risks: adversarial attacks that manipulate models into incorrect or harmful outputs. Security teams must integrate adversarial testing and robust evaluation to ensure AI behaves as expected under attack.

🔹 AI attack detection is ambiguous: In traditional software, security events like unauthorized access or privilege escalation are identifiable. In AI, detecting attacks like model poisoning or adversarial perturbations can be mistaken for natural model drift or training issues, making real-time defense challenging.

🔹 AI access models expand the attack surface: Unlike traditional applications accessed via APIs or libraries, AI can be exposed through web interfaces, increasing the risk of prompt injection and jailbreak attacks. Security controls must account for user interaction risks.

🔹 Continuous Delivery for AI is fundamentally different: DevSecOps CI/CD pipelines typically deploy new code. AI’s CI/CD involves automating continuous training, evaluation, and data collection—introducing new risks if threat monitoring isn’t integrated at every stage.

🔹 Security maturity is lacking: In traditional software, incident response teams (SIRT) manage vulnerabilities and updates. AI security is still catching up, and many organizations lack structured AI-specific security practices, making them blind to emerging threats. The opposite side of a coin is a user that is used to having the service provider staying on top of security incidents and recovering quickly but is quite unaware of the pitfalls of using a poorly managed AI applications. Combine that with regulatory fines that are coming and are bigger than GDPR fines and you have very expensive ramifications for not doing AI right.

🔹 Compute and platform security challenges: AI workloads often run on GPUs, where security best practices are still maturing. Unlike CPUs, GPUs bypass some OS-level protections, and AI models inside containers can still access file systems and networks if improperly configured. Kubernetes security for AI requires specialized expertise.

🔹 Replicating AI vulnerabilities is complex: AI behavior is probabilistic, not deterministic. Unlike software bugs that can be reproduced with a specific input, AI vulnerabilities change as models are retrained. There’s no standardized versioning for model behavior, making red teaming and security assessments significantly harder.

Cognofort: Expert-led AI Security Solutions

At Cognofort, we understand that securing AI is not just about adapting DevSecOps—it requires an entirely new approach. Our mission is to bridge the gap between traditional security practices and AI-specific challenges, ensuring organizations can deploy AI with confidence.

Whether it's securing AI supply chains, mitigating adversarial threats, or integrating robust monitoring into MLOps workflows, Cognofort is at the forefront of AI security innovation.

The AI security challenge is here. Is your organization ready?