How to Ensure Reliable AI Assistants for Cybersecurity with Acalvio’s Deception and RAG Innovations

AI-driven assistants are increasingly used in cybersecurity to analyze threats, provide recommendations, and accelerate SOC operations. While building an assistant that performs well in a controlled environment is achievable, ensuring it stays reliable in real-world, high-stakes environments is far more complex.

Acalvio recently developed a self-hosted Retrieval-Augmented Generation (RAG) system for cybersecurity use cases. Initial results were strong—but as models and frameworks evolved, subtle performance drifts appeared. The system remained fluent, yet its response relevance started to degrade over time, exposing a hidden challenge: small oversights in the retrieval layer can significantly impact performance.

Identifying Retrieval Drift in RAG Pipelines

Through careful evaluation, Acalvio uncovered several hidden issues that affected retrieval quality:

• Embedding Mismatch: An instruction-tuned embedding wrapper was paired with a non-instruction-tuned model, creating inconsistencies in vector representations.
• Similarity Metric Misalignment: A subtle change in how similarity was calculated (e.g., cosine vs. inner product) impacted retrieval precision.
• Missing Filters: Broad queries returned irrelevant results, introducing “noise” into responses.

Individually, none of these caused outright failure—but together they quietly degraded performance, reducing the assistant’s reliability for cybersecurity tasks.

Fixing the Weak Links: Retrieval Layer Enhancements

Once root causes were identified, Acalvio implemented targeted improvements:

• Corrected embedding models and wrappers for alignment.
• Adjusted similarity metrics with proper vector normalization.
• Applied controlled indexing (FLAT) to validate retrieval performance.
• Introduced reranking to reorder results based on semantic relevance.
• Added query expansion to break down complex questions into simpler, more accurate sub-queries.

These refinements not only restored but also improved baseline accuracy metrics:

Recall: 0.9036

MRR: 0.8730

NDCG: 0.8864

The result? A smarter AI assistant that delivers more accurate, actionable answers in cybersecurity contexts.

Why Retrieval Metrics Matter in Cybersecurity

Metrics such as Recall, MRR, and NDCG aren’t just theoretical—they act as guardrails for RAG quality. In cybersecurity, small retrieval errors can translate into:

• Hallucinated or irrelevant answers
• Missed attack indicators
• Misleading context for incident response

By monitoring retrieval-specific metrics, security teams can ensure their AI assistants remain trustworthy, accurate, and effective.

Acalvio + Softprom: Reliable AI for Cyber Defense

RAG systems are powerful—but fragile if not carefully designed and continuously monitored. In cybersecurity, where accuracy is critical, Acalvio’s approach of refining retrieval pipelines, applying deception strategies, and leveraging proactive monitoring ensures AI assistants deliver reliable results at scale.

As an official distributor of Acalvio, Softprom helps enterprises and government organizations adopt these next-generation AI and deception-driven defenses to strengthen their cybersecurity posture.

Contact us to learn how Acalvio’s AI innovations can enhance your cyber defense strategy.