To build an industry factor model, you would need to calculate exposures to industries. If you had GICS (or similar) data available you could use a bottom up approach and calculate those exposures. 1 if the stock belongs to that industry, 0 if not.
In the absence of such data you would need to infer those values. Here is one that I'd suggest (assuming you are looking at the US equity market)
Sector/Industry ETFs (IShares, State Street) are good proxies for industries. Select a set of ETFs that are liquid and encompass the entire industry breadth of the US stock market. Conduct multiple regressions of stock returns on the selected ETF returns over a sufficiently long historical period. Use the obtained coefficients as industry exposures.
The other parts of a risk model that you'd need are the covariance matrix and residual volatility of the instruments. For the covariance matrix use the etf returns over the same or different historical period as the multiple regression above and calculate their covariance. For the residual volatility - use the residuals of the multiple regression above and calculate their standard deviation. You may want to apply some exponential weighting in both the cases.