We fixate on model capability tests – whether a system can generate code, design a molecule, or plan a network intrusion. That focus is necessary, but it can blind us to a simpler, more immediate threat: the safety-testing process itself. Giving outsiders access to powerful models creates a new attack surface, and unless we treat access as the primary system to secure, the tests meant to reduce catastrophic risks may become the vector for them.
The signal: A recent RUSI report highlights a growing paradox in frontier-AI evaluation: meaningful third‑party testing requires broad access, yet each access pathway – API keys, sandboxed internals, or direct infrastructure visibility – multiplies opportunities for theft, tampering, espionage, and misuse. The report’s Access‑Risk Matrix and its conclusion that “write access to internals” is the highest risk are a clear call to move beyond ad‑hoc arrangements.
Why this matters for enterprise architects and CTOs
– Access control is now part of the threat model for AI systems, not an afterthought. Traditional security problems (stolen credentials, poor revocation, over‑privilege) scale in impact when attached to models capable of designing exploits, synthesising toxic content, or revealing proprietary training data.
– The trade‑off is stark: restricting access too heavily blocks rigorous evaluation and slows research; loosening it risks IP loss, model compromise, and national security exposures. This is a classical speed-vs-stability decision, now with higher stakes.
– Governance fragmentation compounds the technical risk. Without a common taxonomy for “secure access,” organisations, evaluators, and regulators negotiate different assumptions, leading to inconsistent protections and gaps exploitable across jurisdictions.
Actionable architecture and policy moves
– Adopt Zero Trust for evaluations. Assume no implicit trust for any external evaluator. Enforce least privilege, short-lived credentials, strong attestation, and automated revocation as standard.
– Define an Access Taxonomy in procurement and SDKs: map the level of access (API-only, log-limited, white-box read-only, white-box write) to required controls (attestation, hardware enclaves, physical presence, multi-party computation).
– Use technical mitigations proportionally: secure enclaves (TEEs), differential privacy for query responses, MPC/SMPC for joint evaluation, formal verification where feasible, and robust logging with tamper-evident audit trails.
– Contractual and operational hygiene: require baseline infosec certifications for evaluators, clear SLAs for incident response, and disallow unchecked data exfiltration by design (e.g., output filters, rate limits).
– Build shared testbeds and synthetic benchmarks. Centralised, vetted evaluation platforms (run by consortia or neutral third parties) can reduce the need for repeated deep access to live systems.
– Plan for adversarial insiders and state-level actors by raising threat modeling beyond standard enterprise profiles – include espionage scenarios in tabletop exercises.
A note for India and public-sector deployments
I have often argued in STPI meetings that India’s Digital Public Infrastructure and government AI procurements must bake these principles into tender documents. For public services using or procuring foundation models, the procurement playbook should require an access-risk assessment, specify minimal technical controls (short-lived credentials, attested hardware), and prefer neutral evaluation platforms to bilateral access deals. In regions like the Northeast, where public trust and interoperability are priorities, a standardized approach reduces both supply‑chain risk and procurement friction.
Three practical takeaways for CTOs and founders
1. Treat evaluation access as code: version it, test it, and revoke it automatically. Don’t hand out long-lived keys for research convenience.
2. Classify models by impact early – use an Access‑Risk Matrix to drive both technical controls and contractual language.
3. Invest in neutral evaluation infrastructure (or join a vetted consortium) rather than bespoke “deep access” arrangements that are hard to scale securely.
Closing thought
Frontier‑AI safety will not be solved by capability tests alone; it will be won or lost in how we govern access to those tests. Secure, standardised access frameworks are the plumbing of safe AI – invisible when working, catastrophic when absent.
About the Author
Sanjeev Sarma is the Founder Director of Webx Technologies Private Limited, a leading Technology Consulting firm with over two decades of experience. A seasoned technology strategist and Chief Software Architect, he specializes in Enterprise Software Architecture, Cloud-Native Applications, AI-Driven Platforms, and Mobile-First Solutions. Recognized as a “Technology Hero” by Microsoft for his pioneering work in e-Governance, Sanjeev actively advises state and central technology committees, including the Advisory Board for Software Technology Parks of India (STPI) across multiple Northeast Indian states. He is also the Managing Editor for Mahabahu.com, an international journal. Passionate about fostering innovation, he actively mentors aspiring entrepreneurs and leads transformative digital solutions for enterprises and government sectors from his base in Northeast India.
