Get a GRiP (Gravitational Risk Procedure) on risk by using an approach inspired by the physics of gravitational forces between body masses! In April 2010, U.S. Department of Homeland Security Special Events staff (Protective Security Advisors [PSAs]) expressed concern about how to calculate risk given measures of consequence, vulnerability, and threat. The PSAs believed that it is not 'right' to assign zero risk, as a multiplicative formula would imply, to cases in which the threat is reported to be extremely small, and perhaps could even be assigned a value of zero, but for which consequences and vulnerability are potentially high. They needed a different way to aggregate the components into an overall measure of risk. To address these concerns, GRiP was proposed and developed. The inspiration for GRiP is Sir Isaac Newton's Universal Law of Gravitation: the attractive force between two bodies is directly proportional to the product of their masses and inversely proportional to the squares of the distance between them. The total force on one body is the sum of the forces from 'other bodies' that influence that body. In the case of risk, the 'other bodies' are the components of risk (R): consequence, vulnerability, and threat (which we denote as C, V, and T, respectively). GRiP treats risk as if it were a body within a cube. Each vertex (corner) of the cube represents one of the eight combinations of minimum and maximum 'values' for consequence, vulnerability, and threat. The risk at each of the vertices is a variable that can be set. Naturally, maximum risk occurs when consequence, vulnerability, and threat are at their maximum values; minimum risk occurs when they are at their minimum values. Analogous to gravitational forces among body masses, the GRiP formula for risk states that the risk at any interior point of the box depends on the squares of the distances from that point to each of the eight vertices. The risk value at an interior (movable) point will be dominated by the value of one vertex as that point moves closer and closer to that one vertex. GRiP is a visualization tool that helps analysts better understand risk and its relationship to consequence, vulnerability, and threat. Estimates of consequence, vulnerability, and threat are external to GRiP; however, the GRiP approach can be linked to models or data that provide estimates of consequence, vulnerability, and threat. For example, the Enhanced Critical Infrastructure Program/Infrastructure Survey Tool produces a vulnerability index (scaled from 0 to 100) that can be used for the vulnerability component of GRiP. We recognize that the values used for risk components can be point estimates and that, in fact, there is uncertainty regarding the exact values of C, V, and T. When we use T = t{sub o} (where t{sub o} is a value of threat in its range), we mean that threat is believed to be in an interval around t{sub o}. Hence, a value of t{sub o} = 0 indicates a 'best estimate' that the threat level is equal to zero, but still allows that it is not impossible for the threat to occur. When t{sub o} = 0 but is potentially small and not exactly zero, there will be little impact on the overall risk value as long as the C and V components are not large. However, when C and/or V have large values, there can be large differences in risk given t{sub o} = 0, and t{sub o} = epsilon (where epsilon is small but greater than a value of zero). We believe this scenario explains the PSA's intuition that risk is not equal to zero when t{sub o} = 0 and C and/or V have large values. (They may also be thinking that if C has an extremely large value, it is unlikely that T is equal to 0; in the terrorist context, T would likely be dependent on C when C is extremely large.) The PSAs are implicitly recognizing the potential that t{sub o} = epsilon. One way to take this possible scenario into account is to replace point estimates for risk with interval values that reflect the uncertainty in the risk components. In fact, one could argue that T never equals zero for a man-made hazard. This paper describes the thought process that led to the GRiP approach and the mathematical formula for GRiP and presents a few examples that will provide insights about how to use GRiP and interpret its results.