TAKEAWAYS
SB 53 is a landmark AI bill: It sets new standards for transparency, incident reporting, and governance that will shape regulations far beyond California.
Impact extends beyond big labs: While only frontier developers will meet current thresholds, CISOs across industries will face supply chain and customer pressure.
CISOs should act now: Map AI assets, align with frameworks like NIST AI RMF, embed continuous red teaming, and implement technical safeguards to future-proof against fast-approaching regulation.
Current status of SB 53
As of September 13, 2025, SB 53 has cleared both legislative chambers and has been publicly backed by Anthropic and its CEO, Dario Amodei.
The bill now sits on Governor Newsom’s desk, with a mid October signing deadline.
After Assembly amendments, it has been reported that the bill requires smaller frontier AI developers (<$500M revenue) to disclose high-level safety testing details, while larger companies must provide more detailed reports. The ripple effects of this would extend across the AI supply chain.
If SB 53 is vetoed, strict AI regulation is still inevitable. States are drafting similar legislation, and federal frameworks are advancing.
In fact, another California bill, SB 243, is awaiting a decision and could soon be signed into law. It targets companion chatbots with safety, transparency, and reporting requirements. This is to protect users - especially minors and vulnerable individuals - from harm in emotionally sensitive AI interactions.
Now’s the time to operationalize AI governance, and avoid costly disruption later.
Core elements of SB 53:
Large frontier developers would be required to:
Publish a frontier AI framework
Clearly post a safety and governance framework describing how they adopt recognized standards and assess/mitigate catastrophic risk.
Ensure model transparency at launch
Publicly disclose model information and summaries of risk assessments/mitigations when releasing covered models - think model card++.
Report critical incidents
Report defined “critical safety incidents” to the state within specified timelines.
Provide whistleblower protections
Protected reporting channels for AI safety concerns from employees and contractors.
Enforcement
Civil penalties (up to $1 million per violation) and Attorney General enforcement for non-compliance.
What does SB 53 mean for CISOs?
You might ask: “If we don’t train foundation models, do I need to worry about this?”
Not directly, but your customers may expect SB 53 style transparency and SB 53 may be a template other states or the federal government adopt.
Supply chain impact
Vendors and AI partners may soon be asked to produce safety documentation.
Executive pressure
Boards will expect clear insights on SB 53 and your AI governance alignment.
Procurement shifts
Buyers of AI-enabled products will start embedding SB 53-style requirements in contracts.
Acting early on AI compliance is a strategic opportunity
SB 53 represents the most significant AI governance shift to date, and early movers will capture outsized value through three mechanisms.
The technical requirements, while sophisticated, create defensible competitive moats.
Organizations that build robust safety testing, monitoring, and governance capabilities will find these investments pay dividends in product quality, and operational resilience.
The vendor management implications create leverage for prepared organizations.
As AI becomes central to competitive advantage, companies with mature AI governance frameworks can move faster in adopting new technologies while maintaining appropriate safeguards.
The transparency requirements build trust that translates directly into business value.
In an era of increasing AI skepticism, organizations that clearly articulate their safety practices and risk management approaches will win customers, attract talent, and secure partnerships
The CISO SB 53 Checklist: compliance, risk, and controls
An actionable roadmap for SB 53, and future AI compliance readiness.
Phase 1: Foundation Building: AI Compliance Reconnaissance
Identify and classify every AI system in your organization: remember to include embedded AI in security tools, marketing platforms, and operational systems. Most organizations discover they have three to five times more AI exposure than initially realized.
Establish a cross-functional AI governance authority committee: with clear accountability lines, final decision owners, regular reporting updates, and executive sponsorship at the CEO and CTO levels.
Phase 2: Integrate and Align the NIST AI Risk Management Frameworks into Existing Compliance Strategy
Draft and publish your AI Safety and Security Framework: use the NIST AI Risk Management Framework as your foundation. Structure it around the four pillars of govern, map, measure, and manage, then layer in your sector-specific requirements. Spell out exactly how you test, mitigate, monitor, and update your AI systems.
Create a model transparency template for any AI you release or operationalize from vendors. This enhanced "model card" should capture supported languages and modalities, intended use cases, known limitations, risk testing summaries, and specific mitigations. Standardize this format across your organization to ensure consistency and completeness.
Update your incident response plan to cover AI critical safety incidents with clearly defined triggers.
Phase 3: Mature AI Risk Management
Add catastrophic risk scenarios to your enterprise risk register. SB 53 centers on events with potential for massive casualties or damages exceeding $1 billion, and identifies your organization's specific "nuclear meltdown scenarios". For most companies however, the catastrophic threshold is much lower than a billion dollars.
Strengthen your data governance and model validation processes. Implement formal safety evaluations and adversarial testing before any production deployment. Maintain a regular "AI red team" cadence for each major release, and ensure your transparency artifacts accurately summarize these results.
Update the existing Third Party / Vendor program to include third-party AI risk in your vendor management program to include specific AI security and governance requirements. Include contractual obligations for incident notification, safety documentation, and cooperation during investigations.
Phase 4: Technical Safeguards
Conduct rigorous safety testing before any model or application deployment. This includes adversarial prompt injection, jailbreak attempts, comprehensive safety evaluation suites, and stress testing for dangerous capabilities.
Implement runtime guardrails after deployment, including output moderation, rate limiting, and anomaly detection.
Monitor for model drift and deceptive behaviors. SB 53 explicitly identifies deceptive behavior that increases risk as a reportable incident category. Develop monitors that detect inconsistency, policy evasion, and capability emergence
Protect model weights and architectures by implementing least-privileged access controls, encryption at rest and in transit, and code-reviewed change management.
Industry-Specific Considerations for SB 53 and AI compliance
AI compliance for technology companies and AI labs:
Expect intense scrutiny of frameworks, model reports, incident reporting, and whistleblower lines.
Be able to answer detailed questions about testing scope, dangerous capability assessment, and incident criteria.
AI compliance for financial services organizations:
Pay close attention to systemic risk and explainability requirements.
Stress-test AI trading and credit decisioning systems for cascading effects that could trigger market instability.
Ensure the governance framework addresses fair lending requirements, incorporates model risk management controls, and maintains human oversight for high-stakes decisions.
When procuring frontier models, require transparency artifacts that align with both SB 53 and existing financial regulations.
AI compliance for healthcare institutions:
Pay close attention to diagnostic and clinical support models.
Beyond standard safety testing, implement continuous monitoring for aggregate harm patterns that might not appear in individual cases.
Enforce HIPAA controls throughout your entire AI pipeline, not just at data ingestion and output points.
Create feedback mechanisms that allow clinicians to easily flag and report potentially unsafe outputs, ensuring rapid response to emerging risks.
What this all means
The AI governance implications from SB 53 extend far beyond California's borders. Federal regulators are paying close attention, and other states are drafting similar legislation. The governance framework you build will position your organization for success across this broader regulatory landscape.
FAQs
Q: How does SB 53 compare to the EU AI Act, and do I need separate compliance programs?
A: There's significant alignment between SB 53 and the EU AI Act, particularly around transparency reporting and risk assessment methodologies. However, SB 53 requires public disclosure of safety policies while the EU AI Act permits private regulatory reporting.
Q: What exactly is a “critical safety incident,” and how fast do we report?
SB 53 focuses on incidents that materially increase catastrophic risk (e.g., dangerous deceptive behavior discovered in testing). The framework includes fast notification, with specific timelines in the bill structure.
Q: Does SB 53 force a universal “kill switch”?
No, SB 53 is focused on transparency and incident duties.
Q: What should go into our published framework?
Map to NIST AI RMF; list your safety testing methods, red-team cadence, third-party review practices, runtime monitoring, incident reporting, and update cadence.
Q: How do we treat third-party AI we embed?
Ask vendors for their published framework and model transparency documentation; add contractual duties to notify you of critical incidents and to cooperate on investigations; verify runtime controls and monitoring on your side.
Q: What happens if Governor Newsom vetoes SB 53?
A: Even if vetoed, the regulatory momentum is in motion. Multiple states are developing similar legislation, and federal frameworks are emerging. Time and resources invested in preparing for comprehensive AI governance will be valuable across any regulatory framework.
SPLX delivers end-to-end AI security - integrating protection, transparency, alignment, and compliance in one platform.
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