AI Threat Analysis for Real-World Systems
AstraEthica conducts adversarial AI threat analysis focused on how AI systems fail under real human use, not idealized test conditions.
Most AI systems perform well on average. Risk emerges at the edges, when language, trust, authority, and social context change faster than models, policies, and safeguards can adapt. These failures are often subtle. Systems appear to function correctly, but small misinterpretations compound into real-world harm.
AstraEthica specializes in identifying pre-incident failure modes that standard benchmarks and static evaluations routinely miss. This includes how AI systems misinterpret evolving language, indirect signaling, authority cues, and social dynamics across platforms and communities.
We work with platforms, public-sector institutions, and high-risk technology teams to surface quiet, compounding failure pathways before they escalate into safety, operational, or reputational incidents.
What We Identify That Standard Evaluations Miss
Most safety systems are optimized for explicit violations and stable risk categories. In real-world environments, risk rarely appears that way.
Our work focuses on failure conditions such as:
- Language and meaning shifting over time
- Coded, indirect, or evasive communication
- Context loss across platforms and communities
- Misinterpretation of trust and authority signals
- Gradual degradation of safeguards that never trigger alerts
These failure modes often remain invisible to traditional monitoring while still producing real-world harm.
Why This Matters for AI Risk and Safety
Meaning evolves. Communities adapt. Platforms reshape how signals are encoded.
AstraEthica analyzes the conditions under which AI safety and trust systems become structurally blind. Rather than treating failures as isolated bugs, we examine how risk emerges across time, platforms, and power imbalances, and where existing pipelines fail to detect it.
This work is designed for environments where speed, ambiguity, and human behavior outpace policy, retraining cycles, and static evaluation frameworks.
Methodology
Our approach combines:
- Adversarial and misuse-oriented evaluation
- Human-centered observation and validation
- Synthetic scenario development
- Privacy-preserving, ethics-first research methods
The goal is not to induce catastrophic failure, but to identify where safeguards degrade gradually and invisibly, long before incidents occur.
Youth and adolescent digital environments serve as a primary proving ground due to fast-moving language, indirect signaling, and high contextual complexity. Patterns identified here often generalize to other high-risk domains, including education, healthcare, and safety-critical systems.
AstraEthica exists to make quiet, compounding AI failures visible early, when they can still be addressed.
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