Random Number Generators: The Tech Behind Fair Play
The coin toss you never see
The reel stops on a wild. A card flips to an ace. A ball settles on black 8. You see the result. You do not see the draw. In that split second, a small engine inside the game picks a number. That number chooses your fate.
If that engine is done right, each round is fresh, blind, and free of bias. If it is done wrong, patterns creep in. That is why we care about random number generators. They are the quiet line between fun and fraud.
What counts as random here?
Short answer: An RNG in gambling is a system that outputs numbers no one can guess ahead of time. Each draw should not depend on past draws. Regulators and labs check this with audits, tests, and rules on how code runs, updates, and logs.
By the end of this guide, you will know the moving parts of an RNG, how labs test them, what “fair” means under law, and how you can spot good signs or bad ones when you pick where to play.
Two families of randomness, one job: fairness
There are two broad ways to make random values. One is software-based: a pseudo random number generator (PRNG). When designed for security, we call it a cryptographically secure PRNG (CSPRNG) or DRBG. The other is hardware-based: a true random number generator (TRNG) that reads noise from the world, like electronic jitter.
Most real-world game systems use a strong CSPRNG at the core and feed it with fresh, high-quality seeds. This is in line with ISO/IEC 18031 guidance on random bit generation. A CSPRNG is fast, easy to deploy in code, and safe when seeded and managed with care.
TRNGs bring “raw” unpredictability. Some even use weather or radio noise. You can see a friendly explainer of how true randomness from atmospheric noise works. In gambling, TRNGs often feed a CSPRNG. The CSPRNG then serves game logic at high speed without leaking state.
RNG types in gambling: how they compare
| PRNG (simple) | Math formula steps through a sequence from a seed | High if weak (e.g., LCG); state can leak patterns | NIST STS, Dieharder; lab audits | Legacy games (not ideal today) | Fast; easy to code | Guessable if seeded or coded poorly |
| CSPRNG / DRBG | Crypto-safe design (e.g., CTR-DRBG, HMAC-DRBG) | Low when seeded and managed well | NIST STS, PractRand; GLI/eCOGRA/BMM certs | Slots, table RNG, live game helpers | Fast; safe; fits audits; easy to scale | Needs careful seeding and reseeding policy |
| TRNG / HRNG | Reads physical noise (thermal, shot, clock drift) | Low (if hardware and health checks are solid) | Lab hardware checks; statistical suites | Seed source for CSPRNG; sometimes direct draws | High entropy; fresh randomness | Hardware cost; speed and bias must be managed |
| Hybrid (TRNG-seeded CSPRNG) | TRNG feeds seeds; CSPRNG serves game draws | Very low when designed right | Full audit trail; STS/Dieharder/PractRand; lab certs | Modern online casinos and studios | Best of both worlds; robust to load | More moving parts; needs good ops |
Notes: Statistical tests flag bias and structure but do not prove security. Sound design, seeding, and code review matter as much as test scores.
Seeds, entropy, and where things go wrong
Good randomness starts with good seeds. Weak seeds break even strong code. The classic guide on this is the IETF’s randomness requirements for security. It urges mixing many sources and guarding state with care.
Real mistakes do happen. One studio once used the server time as a seed. A few sharp users saw repeating cycles on slow games late at night. The fix was simple but key: switch to a CSPRNG and pull seeds from many sources, then reseed on a timer and on key events.
There are also pitfalls in virtual machines. A team can clone a VM, start many game nodes, and by accident reuse the same entropy pool across them. The cryptographic randomness best practices warn about this. The cure is to pull fresh entropy on boot and after a snapshot restore.
Even when you collect noise, you must shape it and watch health. Cloudflare has a good note on why entropy collection and seeding can fail. Good systems track metrics, test raw bits for bias, and fail closed if sources look sick.
“Fair” under regulation: what that means in practice
In regulated markets, fairness is not a slogan. It is a set of rules, checks, and logs. Licenses require certified RNGs, strict change control, and audit trails. Labs retest after code changes. Regulators can pull games if rules are broken.
In Great Britain, the Remote Technical Standards set how remote games should behave. This covers RNG quality, game math, logging, and incident response.
In the EU, the Malta Gaming Authority technical oversight includes controls on RNG use, testing, and game deployment. It also checks supplier chains and hosting setups.
Independent labs like GLI publish norms. See the GLI standards for interactive gaming systems for how they judge RNGs and full systems around them.
Another well-known body is eCOGRA. They define audit scopes and seal rules. Read eCOGRA’s eGAP requirements and RNG certifications for how they assess fairness and controls.
BMM Testlabs is also a core lab used by many operators and studios. Their role is to run independent RNG evaluations and certify builds before launch and after updates.
How labs test RNGs (and what those names mean)
Labs do not take vendor claims at face value. They collect large streams of outputs, run math tests, review code, and check how seeds are made, stored, and rotated. They also look at logs, build steps, and tamper controls.
NIST maintains well-known suites and DRBG guidance. See the NIST Statistical Test Suite and DRBG guidance for what these tests look at: frequency, runs, block patterns, linear complexity, and more.
Dieharder adds classic and extra checks. Many labs or dev teams use the Dieharder battery of tests to spot bias or structure across many gigabytes of data.
Newer stress tools like PractRand stress testing help catch long-range patterns that small samples may miss. Passing tests is not proof of perfect security. But failing a test is a loud alarm.
Inside the black box: a mini lab walk-through
Here is a short, real-to-life flow. A studio sends source and a build. The lab verifies the hash. It compiles on a clean machine. It runs the RNG in isolation and writes outputs to a file. It runs tests. It reviews the code, with a focus on the RNG, seeds, and calls inside game logic. It writes a report with findings and limits.
The lab itself must be qualified. Look for ISO/IEC 17025 accreditation in the lab’s footer or in the certificate. This shows their methods and tools meet global norms.
Lab note (sample data capture)
This is the kind of concise log that helps a reviewer trust the setup. It shows when seeds were mixed, how much data was tested, and what limits are in place.
Field guide for players and reviewers
First, check the site’s footer or help pages. Do you see the lab name (GLI, eCOGRA, BMM) and a current certificate? Click through. Confirm the game or platform name, version, and date. Look for the regulator’s logo and license number. Check if the audit is recent.
Next, read the game info sheet. Is the RNG described? Do they say CSPRNG or TRNG-seeded? Is there a change log? Are there links to test reports? Review sites can help here. For example, the independent team at bestbettingsites.online keeps notes, screenshots, and live links to current seals. That saves time and lets you cross-check claims.
Finally, watch behavior over time. A fair RNG should not show clumps you can bank on. Your sample will be small, so do not make big calls based on a streak. Instead, look for transparency, steady audits, and fast fixes when issues come up.
Myth vs. fact
- Myth: “Casinos can flip a switch and bias the wheel live.” Fact: In regulated markets, code changes need approval and re-cert. Logs and hashes would not match.
- Myth: “PRNGs are fake random.” Fact: A CSPRNG with strong seeds is safe and standard. It is the norm in online games.
- Myth: “Open-source RNGs are unsafe.” Fact: Openness helps audits. The risk is poor use or weak seeding, not openness.
- Myth: “Passing tests means unbreakable RNG.” Fact: Tests catch bias. Good design and ops still matter.
- Myth: “More entropy is always better.” Fact: It must be good entropy, mixed well, and watched for health.
For developers: get it right without pain
Use the platform CSPRNG or a well-known library. Do not write your own RNG. Mix seeds from many sources. Reseed on a timer and on key events. Log seed events (not the seed). Lock build steps and record hashes.
When in doubt, follow the NIST SP 800-90A recommendations. Choose CTR-DRBG or HMAC-DRBG with strong keys. Document the reseed policy. Add health checks and fail closed if they trip.
On common stacks, use proven calls. In C/OpenSSL, that is RAND_bytes for cryptographically strong random values. On Linux, use getrandom(). On modern Java, use SecureRandom with a strong provider. On Node, use crypto.randomBytes(). Keep versions pinned and builds reproducible.
Red flags and quick wins
- Red flag: “Independently tested” with no lab name or link.
- Red flag: Out-of-date certificate or dead link.
- Red flag: “Proprietary algorithm” with no audit trail.
- Red flag: No mention of reseeding or entropy sources.
- Quick win: Verify the cert on the lab’s own site.
- Quick win: Check the last audit date and version number.
- Quick win: Look for clear contact and incident process.
FAQ
Yes, if it is a cryptographically secure PRNG with strong seeds and good ops. This is the standard choice in online games. Labs and regulators accept it when the code, tests, and controls all check out.
PRNGs use math to make a sequence from a seed. CSPRNGs are PRNGs built to resist attack. TRNGs read noise from the world. Many systems use a TRNG to seed a CSPRNG, then use the CSPRNG to serve game draws.
Check for a lab certificate (GLI, eCOGRA, BMM), confirm the version and date, and follow the link to the lab site. Read the regulator’s rules. Trusted review sites can help you find and read these records.
In regulated markets, no—not without leaving traces. Updates need review. Labs and regulators can revoke approval. Logs, hashes, and change control make secret swaps risky and visible.
NIST STS, Dieharder, and PractRand are common. They check for bias, runs, and patterns. Passing is a good sign. But design, seeding, and secure ops are just as important.
They add transparency for users who can verify seeds and results. But they still need solid RNG design, secure seeding, and, in many places, lab audits and a license.
It varies by regulator and lab, but expect checks on major updates and at set intervals. Some platforms show the re-cert date on their help or legal pages.
Glossary (short and useful)
Wrap-up
Fair play rests on sound tech and clear proof. A well-seeded CSPRNG, tested by real labs, and watched by strong ops is the backbone of honest games. As a player, you can verify seals and dates. As a builder, you can follow simple, proven steps. That is how trust is earned—and kept.
Further reading (authoritative): NIST Random Bit Generation project; ISO/IEC 18031 and 17025; UKGC Remote Technical Standards; Malta Gaming Authority; GLI standards; eCOGRA; BMM Testlabs; Dieharder; PractRand; Cloudflare on entropy; OWASP Crypto Randomness Cheat Sheet; IETF RFC 4086.
Responsible play: If gambling stops being fun, seek help. For advice and support, visit BeGambleAware.
Editorial note: We link to primary sources and labs. We have no financial ties to these labs or regulators. If we use affiliate links elsewhere on our site, we mark them.