Security & privacy are essential to human machine interaction; so is the assessment of countermeasures & safeguards. Whereas conventional machine learning systems are evaluated for their recognition performance, projecting the same metrics to security & privacy contexts does not suffice. For security, when countermeasures are added on-top, it misleads to simply consider alone either the original system or the countermeasure: a new system is composed which needs to be evaluated for its entirety. For privacy, where an add-on mindset is inadequate to fulfill by-design and by-default demands, assessment aims at estimating the capacity of an adversary to infer sensitive information from data while having no further knowledge about her. This talk reflects on both at the example of voice biometrics in speech technology. In the ASVspoof and VoicePrivacy challenges, the security & privacy are investigated for speech technology. While the aim of ASVspoof is fake audio detection to protect voice biometrics from attacks through synthetic and replayed speech, the aim of VoicePrivacy is to suppress biometric factors in audio data when only recognition matters of what was said. Both challenges gather new communities for benchmarking solutions with common protocols and datasets at the level of first and advanced steps. For this, task definition is as much of relevance as the development of metrics. Depending on the research challenge, new metrics have been introduced. The tandem detection cost function “t-DCF” and the zero evidence biometric recognition assessment “ZEBRA” frameworks are presented, illustrated, and explained to tackle security & privacy quantification. As an “add-on,” the t-DCF framework extends upon the DCF metric which is used since over two decades in evaluation of voice biometrics. On the contrary, not as “add-on,” the ZEBRA framework is motivated by Shannon’s “perfect secrecy” and the validation methodology of automated systems in forensic sciences. Both frameworks indicate directions for developing future capacities in better characterizing the security & privacy tasks at hand.