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We present Speculative Authorization (SPAN), a prediction technique that reduces authorization latency in enterprise systems. SPAN predicts requests that a system client might make in the near future, based on its past behavior. SPAN allows authorization decisions for the predicted requests to be made before the requests are issued, thus virtually reducing the authorization latency to zero. We developed SPAN algorithms, implemented a prototype, and evaluated it using two real-world data traces and one synthetic data trace. The results of our evaluation suggest that systems employing SPAN are able to achieve a reduced authorization latency for almost 60 percent of the requests. We analyze the tradeoffs between the hit rate and the precision of SPAN predictions, which directly affect the corresponding computational overhead. We also compare the benefits of deploying both caching and SPAN together, and find that SPAN can effectively improve the performance of those systems which have caches of a smaller size.