less academic section intro

contents/core/privacy_security/privacy_security.qmd

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 ## Security Threats to ML Hardware
 
-A systematic examination of security threats to embedded machine learning hardware is essential to comprehensively understanding potential vulnerabilities in ML systems. Initially, hardware vulnerabilities arising from intrinsic design flaws that can be exploited will be explored. This foundational knowledge is crucial for recognizing the origins of hardware weaknesses. Following this, physical attacks will be examined, representing the most direct and overt methods of compromising hardware integrity. Building on this, fault injection attacks will be analyzed, demonstrating how deliberate manipulations can induce system failures.
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-Advancing to side-channel attacks next will show the increasing complexity, as these rely on exploiting indirect information leakages, requiring a nuanced understanding of hardware operations and environmental interactions. Leaky interfaces will show how external communication channels can become vulnerable, leading to accidental data exposures. Counterfeit hardware discussions benefit from prior explorations of hardware integrity and exploitation techniques, as they often compound these issues with additional risks due to their questionable provenance. Finally, supply chain risks encompass all concerns above and frame them within the context of the hardware's journey from production to deployment, highlighting the multifaceted nature of hardware security and the need for vigilance at every stage.
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-@tbl-threat_types overview table summarizing the topics:
+Embedded machine learning hardware plays a critical role in powering modern AI applications but is increasingly exposed to a diverse range of security threats. These vulnerabilities can arise from flaws in hardware design, physical tampering, or even the complex pathways of global supply chains. Addressing these risks requires a comprehensive understanding of the various ways hardware integrity can be compromised. As summarized in @tbl-threat_types, this section explores the key categories of hardware threats, offering insights into their origins, methods, and implications for ML systems.
 
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 | Threat Type             | Description                                                                                      | Relevance to ML Hardware Security              |