How Safe Is Safe Enough? Understanding AI Risk in the Age of Autonomous Systems
- jomargacoscos
- Dec 2, 2025
- 4 min read
Would you trust a fully autonomous system to:
Do your weekly grocery run?
Book your flights?
Send emails on your behalf?
Drive your family across town?
These aren’t hypothetical questions anymore — they increasingly reflect reality, as modern AI systems rapidly take on tasks that once required human judgment, memory, and oversight. As organizations race to adopt automation, an equally important question emerges: How safe is safe enough?
What Humans Need Before They Trust Autonomous Systems
In the well-known self-driving research paper How Safe Is Safe Enough for Self-Driving Vehicles?, researchers found that the average consumer doesn’t want a self-driving system to simply match human performance — they want it significantly safer, often by multiple factors. ResearchGate
This mirrors what we see when humans evaluate risk in automation:
We don’t judge AI on the same scale as humans.
We expect higher reliability, stronger controls, and clearer fail-safes.
Most importantly: we expect a human to be able to intervene.
Because of this gap between human tolerance for risk and what “matching a human” offers, one of the most widely adopted risk-mitigation strategies is human-in-the-loop (HITL) — having a human reviewer who can catch context errors, override flawed decisions, and ensure actions align with business policy and common sense.
However — HITL is only one part of the story.
Key takeaways about how humans evaluate risk in automation:
We don’t judge AI on the same scale as humans.
We expect higher reliability, stronger controls, and clearer fail-safes.
Most importantly: we expect a human to be able to intervene.
Because of this gap between human tolerance for risk and what “matching a human” offers, one of the most widely adopted risk-mitigation strategies is human-in-the-loop (HITL) — having a human reviewer who can catch context errors, override flawed decisions, and ensure actions align with business policy and common sense.
However, HITL is only one part of the story.
When Your Vendor Deploys AI Into Your Organization — Automatically
Keep in mind, many organizations aren't developing AI systems internally — they rely on their existing vendors embedding AI features by default. Consider a hypothetical automation agent that can research internal knowledge bases and automatically send emails on your behalf. On the surface, a productivity breakthrough. But from a security and risk-perspective, vendor-embedded automation introduces serious new concerns. For instance:
If a malicious actor hijacks that workflow — via prompt injection, social engineering, or poisoned content, they could:
Extract internal security documentation.
Exfiltrate personal data belonging to customers.
Generate outbound emails that appear legitimate but contain malicious intent.
And all that — without ever “compromising” an account in the traditional sense. In other words: AI expands the attack surface, not just by creating new vulnerabilities, but by creating new automation pathways that can be exploited.
If this worries you, if you have O365 in your organization, check out the capabilities of Microsoft's Workflows Frontier Agent to which this exact same attack path can be performed.
So, How Can Organizations Address These Risks?
A strong starting point is grounding your approach in emerging AI-safety and governance frameworks that are gaining acceptance across jurisdictions.
Examples include:
NIST AI Risk Management Framework (AI RMF) — developed by the U.S. standards body. It is designed to help organizations manage AI risks across design, development, deployment, and use, promoting trustworthy and responsible AI. NIST
ISO/IEC 42001:2023 — an international standard providing guidance for establishing, implementing, maintaining, and continually improving an “AI management system.” It helps organizations govern AI responsibly across diverse applications. ISO Governance Library
OWASP Top 10 for Large Language Model Applications — a community-driven, security-focused list of the most critical vulnerabilities and risks unique to LLM-based and AI-driven applications (e.g., prompt injection, insecure output handling, data leakage). OWASP Foundation
Industry- or domain-specific guidance — e.g. for finance, healthcare, or critical infrastructure — which may layer additional controls depending on regulatory or operational risk.
These frameworks emphasize core principles that help mitigate risk:
Human oversight and effective intervention (for example, HITL).
Robust safeguards and output validation.
Monitoring, auditability, and explainability of AI outputs.
Secure design and resilience to adversarial threats.
Minimizing harmful, unauthorized, or unintended actions (data leakage, mis-information, unauthorized access, etc.)
Together, they form the backbone of an AI-readiness and risk-governance strategy — helping ensure automation increases value without unintentionally creating compliance exposures or security gaps.
Where Venra Labs Can Help
At Venra Labs, services offerings include:
Assessing an organization’s AI-risk posture and identifying gaps.
Implementing practical governance controls tailored to your environment.
Evaluating the safety of vendor-embedded AI capabilities.
Designing safe, reliable, human-centered AI workflows that balance automation gains with human oversight and compliance.
If you’d like to learn more about AI risk, governance, and how to safely roll out autonomous systems across your organization — feel free to reach out to us via LinkedIn!
Stay safe! Meaning... keep a human-in-the-loop for now?
Comments