Security teams face a new class of threats (prompt injection, data poisoning, model drift, RAG abuse, and agentic attacks) that require qualitative, evidence-led analysis. This post refracts InfoQ’s Jun 29, 2026 expert panel through an AI-enabled qualitative research workflow so security analysts and researchers can turn narrative incidents into repeatable intelligence. Follow the short playbook and see how www.evidano.com accelerates transcription, thematic coding, cross-segment comparison, and incident forensics.
Findings snapshot
| Date / Item | Metric / Value | Source / Note |
|---|---|---|
| Published | Jun 29, 2026 | InfoQ panel (see source) |
| Panelists | 4 experts | Arshad, Allani, Dilwale, Maljkovic |
| Top threats called out | Prompt injection, data poisoning, RAG abuse, agent/tool abuse, AI-augmented social engineering | Panel consensus |
| Recommended focus | Behavioral monitoring, AI threat modelling, cross-functional IR | From multiple panelists |
AI security threat analysis: what the panel found
The panel reframes security from deterministic vulnerabilities to probabilistic, goal-directed failures. The panel advises that teams must ask: what inputs influenced the model, what was retrieved, which tools were called, and what human or automated actions followed?
points: ['Prompt injection and indirect injection exploit trust at component boundaries (user input → system instruction; retrieved doc → model context).', 'Data poisoning and poisoned OSS can propagate insecure patterns into developer workflows via AI coding assistants.', 'AI-augmented social engineering scales persuasion with tailored phishing, voice deepfakes, and automated follow-ups.', 'Agentic and connector abuse is especially dangerous because an agent with legitimate permissions can exfiltrate or perform actions that appear normal.'],
Do more, faster with Evidano (mapped to this use case)
From messy incident notes to evidence packs
Evidano transforms fragmented incident data into organized evidence packs. Problem: IR collects fragmented data (chat logs, PDFs, ticket threads, audio notes) that are hard to synthesize. Evidano ingests transcripts, PDFs, emails, and connector logs, and generates time-stamped thematic and quote-level extracts so analysts can reconstruct the chain of influence (who said what, when, and which document triggered behavior).
Trace qualitative threads: prompts → retrieval → action
Evidano helps analysts follow the causal text that influenced model behavior. Problem: Panelists stress the need to reconstruct what the model saw (retrieval traces, prompt framing). Evidano automatically tags retrieved-doc excerpts, builds co-occurrence networks, and applies hierarchical codes so analysts can follow a narrative across sources and surface the exact text that likely influenced an agent.
Cross-segment comparisons for hypothesis testing
Evidano enables reproducible comparisons across groups and versions. Problem: You need to know whether a failure was systemic or specific to a segment (team, environment, model version). Evidano runs cross-segment frequency analysis and comparative thematic reports to test whether an event happened more with connector A versus B and to quantify drift over time.
Speed up red-team & forensic cycles
Evidano accelerates red-team iterations and forensic turnarounds. Problem: Manual coding slows red-team iterations and delays fixes. Evidano imports codebooks, applies AI-assisted coding, surfaces representative quotes, and produces ready-to-share visualizations (word clouds, co-occurrence graphs) to shorten the time from finding to mitigation.
Security & compliance guardrails
Evidano enforces strict handling for sensitive incident material. Problem: Sensitive incident material requires strict handling. Evidano provides enterprise encryption, PII redaction, and a no-third-party-training guarantee to keep analyses private while enabling team collaboration.
FAQ: AI security threat analysis
What is qualitative AI security threat analysis and when should I use it?
Qualitative AI security threat analysis is a structured approach to analyze text- and evidence-heavy incidents to explain causal chains. Use it for incidents with ambiguous causes, repeated drift, or agentic misbehavior, where prompts, retrieved documents, and human context are central to understanding the failure.
How do I compare segments reliably?
Use reproducible codebooks and normalized inputs to compare segments reliably. Reproducible codebooks, normalized timestamps and model-version metadata, and cross-segment frequency and co-occurrence analyses surface statistically meaningful differences.
How secure is the analysis platform?
Evidano supports enterprise security controls suitable for sensitive incident work. Evidano supports enterprise encryption, PII redaction, role-based access, and a no-third-party-training guarantee.
Wrapping up & next steps
AI threats are evolving toward subtle, systemic manipulation, cross-system prompt manipulation, agent chains, and AI-augmented social engineering, and the practical response is evidence-first investigation. The practical response is to assemble prompts, retrieval traces, tool calls, and human context, then run reproducible qualitative analyses to inform policy and containment.
Try this on your next AI incident by ingesting a single case into Evidano, running thematic and cross-segment analyses, and exporting an evidence pack for IR. Get started at Try Evidano for free and use the panel notes from InfoQ as a checklist.