The growing reliance on artificial intelligence (AI) for security applications ranging from biometric identification to predictive policing has raised pressing concerns about misprofiling, bias, and disproportionate targeting of marginalized populations. Misprofiling in security AI arises from skewed datasets, opaque algorithmic models, and exclusion of affected communities in the design process, leading to systemic errors and erosion of public trust. To address these challenges, participatory design (PD) emerges as a critical approach that embeds inclusivity, accountability, and contextual knowledge directly into AI development pipelines. Unlike conventional top-down engineering methods, PD emphasizes collaboration with end-users, stakeholders, and communities most impacted by surveillance and risk assessment technologies. This democratization of design enables the identification of hidden biases in training datasets, ensures diverse perspectives shape model objectives, and creates space for ethical negotiation in algorithmic trade-offs. Moreover, PD can be operationalized through structured workshops, co-design prototyping, and iterative feedback mechanisms, all of which promote transparency and foster legitimacy in security AI systems. When applied rigorously, participatory design not only mitigates the risk of misprofiling but also enhances operational reliability by aligning technical outputs with real-world security needs. At a broader scale, the adoption of PD frameworks contributes to governance models that balance innovation with human rights protections. This paper argues that participatory design is not merely a supplementary tool but a structural remedy to the persistent failures of misprofiling in security AI. Its integration into design and policy cycles is essential for achieving both technological robustness and societal fairness.