We adjust the Proof of Work (PoW) consensus mechanism used in Bitcoin and Ethereum so that we can build on its strength while also addressing, in part, some of its perceived weaknesses. Notably, our work is motivated by the high energy consumption for mining PoW, and we want to restrict the use of PoW to a configurable, expected size of nodes, as a function of the local blockchain state. The approach we develop for this rests on three pillars: (i) Proof of Kernel Work (PoKW), a means of dynamically reducing the set of nodes that can participate in the solving of PoW puzzles such that an adversary cannot increase his attack surface because of such a reduction; (ii) Practical Adaptation of Existing Technology, a realization of this PoW reduction through an adaptation of existing blockchain and enterprise technology stacks; and (iii) Machine Learning for Adaptive System Resiliency, the use of techniques from artificial intelligence to make our approach adaptive to system, network and attack dynamics. We develop here, in detail, the first pillar and illustrate the second pillar through a real use case, a pilot project done with Porsche on controlling permissions to vehicle and data log accesses. We also discuss pertinent attack vectors for PoKW consensus and their mitigation. Moreover, we sketch how our approach may lead to more democratic PoKW-based blockchain systems for public networks that may inherit the resilience of blockchains based on PoW.