AI Security
Protecting Your Models, Data & AI Workflows
From Inception to Runtime
AI introduces fresh risks across your stack — models, pipelines, data flows, and end user access. You’re facing challenges such as:
- Shadow or unsanctioned AI apps leaking sensitive data
- Vulnerabilities inside models (prompt injection, drift, poisoning)
- Runtime manipulations, adversarial attacks, or model abuse
- Lack of visibility over deployed models and their behavior
- Integrating AI security across cloud, on-prem, edge
- Bridging development, operations, and security for AI systems
If AI security is an afterthought, you risk data leakage, model corruption, regulatory exposure, and trust breakdown.
AI Challenges
Shadow / Unauthorized AI Tools
Employees adopt AI tools (chatbots, assistants, generative apps) outside IT oversight, risking data leakage and misuse.
Model Vulnerabilities & Weakness
Models may contain embedded vulnerabilities — for example prompt injection, dataset poisoning, or overfitted bias paths.
Runtime Attacks & Misuse
Once running, models are vulnerable — adversarial prompts, malicious inputs, use beyond intent, or data exfiltration.
Behavioral Drift / Model Corruption
Over time, models may drift — their outputs deviate or degrade, or they get corrupted via training cycles.
Lack of Visibility / Lineage
In complex AI ecosystems, it’s often unclear which data, model, pipeline, or version contributed to a given inference or decision.
Security Lagging DevOps / MLOps
AI development cycles are fast, iterative, and often bypass traditional security checks.
Subnetik Solutions
Shadow / Unauthorized AI Tools
Automatically detect, inventory, classify, and apply access restrictions to all AI apps across your environment. (Cisco’s AI Defense supports this capability).
You reclaim control and visibility over the AI surface exposed in your organization.
Model Vulnerabilities & Weakness
Use algorithmic red-teaming and automated validation to uncover vulnerabilities before deployment. Enforce guardrails tailored per model.
Models are validated and secured before exposure, reducing risk at launch.
Runtime Attacks & Misuse
Embed guardrails and runtime protection to monitor, intercept, or block suspicious usage or model behavior as it executes.
Threats active at runtime are caught and mitigated, not just flagged after the fact
Behavioral Drift / Model Corruption
Continuously monitor model behavior, detect deviations, flag or retrain when drift or anomalies occur.
Maintains fidelity and correctness over the model’s lifetime.
Lack of Visibility / Lineage
Maintain full lineage, telemetry, usage logs, and metadata across model training, data, deployment, and inference cycles.
You can audit, debug, attribute, and track AI decisions end-to-end.
Security Lagging DevOps / MLOps
Integrate security into CI/CD / MLOps pipelines — APIs, policy enforcement, automated checks, guardrail frameworks.
Security becomes a seamless part of AI development, not a bottleneck.
Why This Secure Ai Approach Works
- Holistic Protection Across AI Lifecycle — From model building and deployment to runtime and reuse.
- Control Over Shadow AI Use — Discover and govern AI tools before they become a liability.
- Runtime Safety & Integrity — Guardrails that monitor and enforce safe behavior in real time.
- Proactive Vulnerability Mitigation — Validation and red-teaming catch weak points before exploitation.
- Full Traceability & Accountability — Lineage and telemetry provide audit paths and insight.
- Developer-Friendly Security — Embedded checks, APIs, and pipeline integration make security frictionless.
