Saw a design with battery / super capacitor and light sensor to deal with that. On light detect, kill power to volitile RAM and or wipe non volatile storage.
Cat n mouse game level up! Now it has to be done in the dark, or using a light wavelength invisible to the sensor.
or using a light wavelength invisible to the sensor.
...such as x-rays.
There are some highly secure (and expensive) cryptoprocessors which detect those too, but since security is their primary function, the effort is worth it. Otherwise you just end up spending more on protecting something of relatively little value that the cloners will reimplement in some other way anyway.
After some initial cliffs (e.g. higher-end crypto locked MCUs) at the end of the day security becomes an economic cat-and-mouse game. For cases where there's physical access to the devices at least.
I worked with a guy for a while doing high end security for buildings measured in 10's of K dollars per square foot. He was on a sort of forced retirement and had to have some work for a year or two gap before going on Social Security.
We had some amazing conversations about things like home security, say the value of a dog compared to fairly expensive systems, and the overall basic equation below as applied to threat scenarios:
Basically, when:
entry_cost < ((rewards / risks) + cost_of_sale)
...there is a chance that attempts to infringe will happen and be successful. When the risk/reward equation ends up being positive multiples of entry_cost valuation, you can count on infringement happening.
The whole game is to make that equation unfavorable and then stay ahead of all that as part of the normal product development and revisions over time, including new features, bug fixes, and the like. Obviously, strong customer relationships and value added can only strengthen the moat here. These basically increase that cost_of_sale term considerably.
Entry_cost is simply everything they need to do in order to infringe.
Rewards are a simple sales forecast, what's the revenue coming in going to look like, and why?
Risks get added up and weighted by percentage estimated likelihood to happen.
On this one, it's very similar to how sales opportunity is valued and rolled up into a forecast:
opp1 = $100k * 1 (gonna happen, done deal, no exceptions)
opp2 = $1M * 0.1 (high risk, many exceptions to overcome)
The two opps above are worth the same potential revenue! ...in terms of a forecast for that sales period, quarter, whatever. Sales teams that do not weigh their forecasts often under-perform and or show very turbulent performance relative to expected performance. Ideally, as opp2 is improved, sales exceptions removed, the degree of certainty improves, leading to more potential, real revenue in future forecasts.
Cost_of_sale is basically what it takes to do business and obtain that revenue.
That basic analysis formula has worked well in my experiences so far.
