DARPA and IBM are ensuring that anyone can protect their AI systems from hackers
News
Mike Murphy
Data and AI Security
As organizations move to the hybrid cloud, they must protect sensitive data and comply with regulations that allow them to take advantage of AI. We’re designing systems to monitor and protect data, building trust in AI through robust evaluation, certification, and hardening against attacks.
Our work
An AI model trained on data that looks real but won’t leak personal information
ResearchKim MartineauAn open-source toolkit for debugging AI models of all data types
Technical noteKevin Eykholt and Taesung LeeSimplifying cloud security policies with AI
Technical noteJulian Stephen and Shriti PriyaProtect yourself from bad neighbors in the cloud
Technical noteMichael V. Le, Salman Ahmed, Dan Williams, and Hani JamjoomAI diffusion models can be tricked into generating manipulated images
NewsKim Martineau- See more of our work on Data and AI Security
Publications
Challenges and Remedies of Domain-Specific Classifiers as LLM Guardrails: Self-Harm as a Case Study
- Bing Zhang
- Guang-Jie Ren
- 2025
- NAACL 2025
SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging
- Aladin Djuhera
- Swanand Ravindra Kadhe
- et al.
- 2025
- ICLR 2025
Rhychee-FL: Robust and Efficient Hyperdimensional Federated Learning with Homomorphic Encryption
- Yujin Nam
- Abhishek Moitra
- et al.
- 2025
- DATE 2025
Workshop on Data Integrity and Secure Cloud Computing (DISCC)
- Pradip Bose
- Augusto Vega
- et al.
- 2025
- HPCA 2025
Quantifying the Bit-Error Resilience of FHE Compute
- Augusto Vega
- Matías Mazzanti
- et al.
- 2025
- HPCA 2025
Automated Expansion of Privacy Data Taxonomy for Compliant Data Breach Notification
- Yue Qin
- Yue Xiao
- et al.
- 2025
- NDSS 2025
IBM Solution: IBM Cloud Pak for Data
Our research is regularly incorporated into new security features for IBM Cloud Pak for Data.
Learn more