The 6 Challenges Your Business Will Face in Implementing MLSecOps

Organizations that don't adapt their security programs as they implement AI/ML run the risk of being exposed to a variety of threats, both old and emerging ones. MLSecOps addresses this critical gap in security perimeters by combining AI and ML development with rigorous security guidelines. Establishing a robust MLSecOps foundation is essential for both proactively mitigating vulnerabilities and simplifying the remediation of previously undiscovered flaws.

Challenge 1: Defining the Unique, Changing Threat Landscape

AI/ML systems come with a host of new threat vectors that security teams need to consider in addition to their existing processes. These might include data poisoning, adversarial inputs, model theft or tampering, or even privacy-specific attacks like model inversion and membership inference. Defending against these new threats means creating controls designed specifically for the ML lifecycle.

  • Stress testing models is crucial
  • Security professionals must be prepared for repeated, probing attacks instead of covert one-time hacking attempts

Challenge 2: The Hidden Complexity of Continuous Training

AI models evolve, which adds another layer of complexity to MLSecOps security. Each time a model is trained and retrained on data, new vulnerabilities are potentially being introduced to the ML ecosystem. To combat this, each retraining of the model should be treated as a net-new product version.

  • IT and security leadership might even consider creating materials to accompany the latest version of their model – much like app makers share version details with each new release
  • If model training is not continuously tracked, the security of MLSecOps programs will drift over time

Challenge 3: Managing Opacity and Interpretability in ML Models

ML models are often “black boxes”, even to their creators, so there’s little visibility into how they arrive at answers. For security pros, this means limited ability to audit or verify behavior – traditionally a key aspect of cybersecurity.

  • Trusted Execution Environments (TEEs) can help circumnavigate this opacity
  • TEEs enable organizations to use attestation data to build pre-established standards and guides for appropriate model behavior

Challenge 4: Creating a Secure Training Data Pipeline

Models are not static and are shaped by the data they ingest. Thus, data poisoning is a constant threat for ML models that need to be retrained. Organizations must embed automated checks into the training process to enforce a continuously secure pipeline of data.

  • Using information from the TEE and guidelines on how models should behave, AI and ML models can be assessed for integrity and accuracy each time they are given new information
  • Security leaders should regularly enforce checks on the resilience of their MLSecOps program

Challenge 5: Difficulties in Performing Risk Assessment

Risk assessment frameworks that work for traditional software will not be applicable to the changeable nature of AI and ML programs. Traditional assessments fail to account for tradeoffs specific to ML, e.g., accuracy vs fairness, security vs explainability, or transparency vs efficiency.

  • Businesses must evaluate models on a case-by-case basis, looking to their mission, use case and context to weigh their risks
  • Cross-functional collaboration is key to assessment, involving ML engineers, security teams and policy leaders

Challenge 6: Balancing Security and Efficiency

The ability to retest a model and achieve comparable results will preserve trust in that model's progression. This requires ongoing effort from security teams to ensure the integrity of their MLSecOps program.

  • Creating a lineage of a model, which will allow security teams to have visibility into version control and changes to the model over time
  • Aiming for approximate reproducibility when exact reproduction isn’t possible due to the changing nature of the model and its data