SC-30(2): Randomness

SC-30(2) requires you to intentionally add randomness to selected system operations and assets so attackers cannot reliably predict, replay, or precisely time their actions. To operationalize it quickly, define where randomness matters in your environment (crypto, tokens, scheduling, address/layout, identifiers), implement approved randomization techniques, and retain evidence that the techniques are deployed, monitored, and governed. ¹

Key takeaways:

• Scope first: document which operations/assets require randomness and why, then implement randomization techniques consistently.
• Treat randomness as an engineering control with governance: owners, triggers, exceptions, and recurring health checks.
• Evidence must show both design (what you intended) and operation (what is running today), including configuration and test results.

SC-30(2) “Randomness” is a requirement that often gets lost between crypto discussions and general “secure engineering” expectations. Auditors and customer assessors still ask for it because it is a practical way to reduce predictability across your stack, which lowers the success rate of entire classes of attacks (replay, brute force, targeted timing, and exploitation that depends on stable memory layouts or identifiers). The control is written broadly on purpose: it lets you choose the techniques that match your system’s risk and architecture rather than prescribing a single mechanism. ²

For a CCO or GRC lead, the fastest path is to convert SC-30(2) into an implementable “control card” your engineering teams can execute: define the specific assets and operations that need randomization, specify the acceptable randomness sources and libraries, set build/release gates, and identify the operational evidence that proves the control is working. If you do only one thing, do this: make randomness an explicit requirement in engineering standards and system configuration baselines, then validate it with repeatable tests and logs.

Regulatory text

Requirement (excerpt): “Employ [techniques] to introduce randomness into organizational operations and assets.” ¹

What the operator must do

You must (1) select appropriate randomization techniques for your environment, (2) implement them in the systems and workflows where predictability creates security risk, and (3) maintain proof that the randomization is in place and remains in place through change. The bracketed “[techniques]” is a signal that you decide the methods, but you still need a defensible rationale and evidence trail. ¹

Plain-English interpretation (what SC-30(2) really expects)

Your systems should not behave in perfectly predictable ways when predictability helps an attacker. That can mean:

• Cryptographic randomness (keys, IVs/nonces, salts)
• Security tokens and session identifiers that cannot be guessed
• Randomized scheduling or jitter (where appropriate) to reduce timing predictability
• Random identifiers to reduce enumeration (e.g., object IDs, reset links)
• Platform-level randomization (e.g., memory layout randomization) where supported

This is not “add randomness everywhere.” It is “add randomness where it breaks attacker playbooks without breaking reliability, performance, or traceability.”

Who it applies to (entity and operational context)

SC-30(2) is commonly scoped to:

• Federal information systems and contractor systems handling federal data (including systems supporting federal contracts). ¹

Operationally, it applies anywhere your organization:

• Builds or configures applications, APIs, services, or authentication systems
• Operates cryptography (TLS termination, key management, secrets handling)
• Manages identity and access (sessions, tokens, password resets, MFA workflows)
• Runs infrastructure where platform protections can be enabled (OS/container hardening)

What you actually need to do (step-by-step)

1) Create a control card (owner, scope, triggers, exceptions)

Write a one-page SC-30(2) control card that includes:

• Control objective: reduce predictability in defined operations/assets
• Control owner: usually AppSec/Platform Security; GRC owns oversight
• In-scope systems: list by environment boundary (prod first)
• Trigger events: new system, major release, crypto change, auth change, platform baseline change
• Exception rules: documented risk acceptance with compensating controls and expiry

This is the fastest way to prevent SC-30(2) from turning into a policy-only statement.

2) Build an “inventory of predictability” (where randomness is required)

Run a short working session with AppSec + platform + IAM and produce a table like this:

Area	What must be random	Minimum expectation	Where to enforce
Cryptography	keys, nonces/IVs, salts	generated by approved RNG; no reuse	KMS/HSM, crypto libs, code review
Auth/session	session IDs, refresh tokens	unguessable, high entropy	IAM service, auth middleware
Account recovery	reset tokens/links	single-use, short-lived, unguessable	identity workflows
Identifiers	external object IDs	non-sequential to prevent enumeration	API layer, DB mapping
Platform	memory/layout	enable supported ASLR / hardening	OS baseline, container images

You are not required to implement every row for every system. You are required to decide what applies, and prove the decision.

3) Standardize approved techniques (engineering guardrails)

Define “approved randomness techniques” for your environment. Keep it practical:

• Approved RNG sources and libraries ¹
• Rules for token generation (where generated, how stored, rotation/expiry handling)
• Prohibited patterns (e.g., time-based seeds, predictable UUID versions for security tokens, “random” based on math PRNGs not meant for security)
• Configuration standards for platform randomization features where supported

Publish this as an engineering standard and reference it from secure SDLC requirements.

4) Implement enforcement points (make it hard to do the wrong thing)

Pick enforcement points that match how your teams build:

• CI/CD checks: static checks for banned randomness APIs, required calls for token creation paths
• Code review requirements: security sign-off for crypto/auth changes
• Baseline configuration: hardened images with required OS settings enabled where applicable
• Central services: issue session tokens from one well-governed service instead of re-implementing per app

This is where SC-30(2) becomes measurable.

5) Validate randomness in operation (tests you can repeat)

Auditors will not accept “we use a secure library” if you cannot show it is actually used in the relevant code paths and configurations.

Validation methods you can operationalize:

• Config scans showing required platform options enabled (where applicable)
• Automated unit/integration tests that verify token format, length/entropy expectations, and one-time use logic
• Cryptographic implementation reviews for sensitive components (auth gateway, KMS integrations)
• Runtime checks: logging/metrics that detect abnormal token collision rates or repeated nonces (where feasible)

Keep the tests aligned to risk. Don’t overbuild.

6) Define the evidence bundle and retention location

Treat SC-30(2) like a control with an evidence packet per review cycle:

• Inputs (standards, approved libraries list, scoping decisions)
• Approvals (security architecture sign-off, exception approvals)
• Outputs (configs, CI checks, test results, scan reports)
• Ongoing proof (change tickets, release notes, control health check results)

If you use Daydream to manage third-party and internal control evidence, store the control card, the “inventory of predictability,” and each validation report as the minimum bundle so you can answer customer diligence quickly without reconstructing history.

7) Run recurring control health checks and track remediation to closure

Operationalize “still true” checks:

• Are new services using the standard libraries?
• Did any team add a custom token generator?
• Did base images drift from hardened configuration?
• Are exceptions expired or still justified?

Track findings, assign owners, and close with validation evidence.

Required evidence and artifacts to retain (audit-ready)

Keep these artifacts in a system of record (GRC tool, ticketing system, or evidence repository):

1. SC-30(2) control card (owner, scope, cadence, triggers, exceptions)
2. System/applicability register mapping randomness requirements to systems/components
3. Engineering standard for randomness (approved RNG sources, prohibited patterns)
4. Configuration baselines (hardened images, OS settings, platform controls)
5. CI/CD enforcement evidence (pipeline configs, screenshots/exported results, policy-as-code)
6. Test evidence (unit/integration tests, scan reports, crypto review notes)
7. Exception register with compensating controls and expiry dates
8. Control health check logs and remediation tickets through closure

Common exam/audit questions and hangups

Expect these questions from assessors and customer security reviews:

• “Which systems are in scope for SC-30(2), and why?”
  Hangup: teams can’t explain scoping decisions beyond “we do secure coding.”

• “Show me where randomness is generated for session tokens / reset links.”
  Hangup: token generation is scattered across services, with inconsistent libraries.

• “How do you prevent regressions?”
  Hangup: no CI checks, no baseline scanning, no repeatable validation.

• “What do you do if a team can’t implement the standard technique?”
  Hangup: exceptions exist but lack expiry, compensating controls, or approval evidence.

Frequent implementation mistakes (and how to avoid them)

1. Treating SC-30(2) as crypto-only
   Fix: include identifiers, tokens, and operational predictability (timing, enumeration) in your scoping workshop.

2. Allowing app-by-app token generation
   Fix: centralize token issuance or publish a vetted internal library with enforced adoption.

3. No evidence of operation
   Fix: require a recurring evidence export (pipeline results, config scans, test runs) tied to release cadence.

4. Confusing “random-looking” with “security-grade randomness”
   Fix: write a short “approved RNG” standard and enforce it through code review and CI checks.

5. Exceptions that never die
   Fix: time-box exceptions with an expiry and require re-approval with updated risk rationale.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for SC-30(2), so you should treat this as a control that is primarily enforced through:

• federal ATO/assessment expectations,
• contractual security requirements,
• and customer diligence questionnaires tied to NIST SP 800-53 alignment. ²

Risk implications are straightforward: predictable tokens, identifiers, or cryptographic artifacts can enable account takeover, data exposure through enumeration, and replay attacks. From a governance perspective, the bigger risk is failing an assessment because you cannot demonstrate ownership, scope, and evidence of ongoing operation.

Practical 30/60/90-day execution plan

First 30 days (establish control ownership + scope + standards)

• Assign control owner and publish the SC-30(2) control card.
• Run the “inventory of predictability” workshop and produce the in-scope register.
• Publish approved randomness techniques and prohibited patterns for your main stacks.
• Identify high-risk flows (auth/session, password reset, API identifiers) for immediate validation.

Days 31–60 (enforcement + initial validation)

• Implement CI checks and code review gates for randomness-sensitive components.
• Standardize token generation via a shared service/library for new development.
• Enable baseline scanning for platform-level randomization where supported.
• Collect the first evidence bundle and store it in your evidence repository (or Daydream) with clear labeling by system and date.

Days 61–90 (operate the control + close gaps)

• Run the first control health check across all in-scope systems.
• Remediate any nonstandard token generation, weak randomness sources, or baseline drift.
• Formalize the exception process and clean up any “informal exceptions.”
• Prepare an assessor-ready narrative: scope, techniques chosen, validation results, and how you prevent regressions.

Frequently Asked Questions

Do we have to add randomness to every system and process?

No. You need to choose techniques that introduce randomness into the operations and assets where predictability creates risk, then document scope and rationale. The control expects deliberate selection and evidence of implementation. ¹

Does SC-30(2) mean we must use a hardware RNG or HSM everywhere?

The requirement does not prescribe a specific source. Define approved randomness sources per your environment and apply them consistently to crypto and security-sensitive tokens, with evidence that teams follow the standard. ¹

What’s the fastest audit-ready evidence set for SC-30(2)?

A control card, a scope/register mapping randomness requirements to systems, an approved-techniques standard, and one set of validation outputs (CI results, config scans, test runs) tied to recent releases. Keep exceptions and approvals in the same evidence trail.

How do we handle legacy apps that use weak or inconsistent token generation?

Put them on an exception with compensating controls and an expiry, then migrate token issuance to a shared library or centralized service. Track remediation tickets to closure and retain before/after evidence.

How should we test “randomness” without becoming a research project?

Focus on practical verification: confirm approved APIs/libraries are called in the right code paths, confirm tokens are unguessable by design, and confirm configs haven’t drifted. Keep tests repeatable and tied to release pipelines.

Where does Daydream fit if this is a technical control?

Daydream is useful for making SC-30(2) auditable: store the control card, scoping register, exception approvals, and each recurring evidence bundle in one place. That reduces scramble during assessments and customer questionnaires.

Footnotes

1. NIST SP 800-53 Rev. 5 OSCAL JSON

2. NIST SP 800-53 Rev. 5