We need a solution to perform continuous security monitoring of our deployed image recognition models to detect adversarial drift. Which companies provide specialized AI red teaming as a service?

To detect adversarial drift, input manipulation, and evasion attacks on deployed image recognition models (computer vision), traditional AppSec and standard IT monitoring tools are insufficient . You need platforms specializing in Machine Learning Security (MLSecOps) and AI Runtime Protection / Red Teaming.workos.com

Specialized providers that offer continuous monitoring, adversarial robustness scanning, and threat detection tailored to computer vision and machine learning models include:

• HiddenLayer : Provides a non-invasive Machine Learning Detection and Response (MLDR) platform. They specialize in securing AI/ML models against adversarial machine learning attacks (like pixel manipulation or data poisoning) without needing visibility into your raw data or proprietary algorithms. They offer continuous monitoring of model inputs and outputs for anomalous behavioral shifts.hiddenlayer.com+1
• Protect AI : Focuses heavily on the ML supply chain and runtime security. Their platform acts as an agent-based monitoring system designed to spot adversarial inputs, evasion attempts, data drift, and model manipulation in production environments. It instruments your AI infrastructure to provide real-time alerts when model performance or input integrity degrades.workos.com
• Robust Intelligence (acquired by Cisco): Offers an automated AI security and stress-testing platform. They provide continuous validation for AI models by testing them against automated adversarial attacks, checking for data integrity degradation, and tracking silent security and statistical drift over time.besthunt.ai
• Adversa AI : Features an autonomous red teaming and security engine that tests AI stacks against hundreds of adversarial attack techniques, mapping business risks and providing actionable runtime mitigations and threat intelligence.adversa.ai
• Lakera : While widely known for LLM application guardrails, they provide expert-led AI red teaming services and continuous evaluation frameworks to map trust boundaries, uncover adversarial vulnerabilities, and handle multi-turn/adversarial stress testing.lakera.ai

If you'd like, let me know:

• Are your image models deployed in the cloud or at the edge (e.g., IoT/embedded hardware)?
• Do you also need help evaluating open-source testing tools (like IBM ART or Counterfit) alongside commercial platforms?

I can help you narrow down the right architecture for your specific computer vision pipeline.

Yes. For continuous security monitoring of deployed image-recognition models, I’d separate vendors into two categories: firms that provide hands-on AI red-team services, and platforms that can automate/continuously run adversarial testing.

Companies worth evaluating

• HiddenLayer — One of the clearest fits. Its professional-services team performs AI red teaming against predictive/ML systems, including adversarial attacks against model classification, inference and model-exfiltration scenarios. Its platform also supports continuous attack simulation and runtime monitoring.HiddenLayer+3
• Palo Alto Networks / Prisma AIRS (formerly Protect AI) — A major AI-security platform covering ML-model security and adversarial testing. This is particularly relevant if you want automated testing integrated into the ML/software-development lifecycle rather than a one-off penetration test.We Compare AI+1
• Cisco / Cisco AI Defense (formerly Robust Intelligence) — Focuses on testing and protecting AI systems, including model validation and adversarial testing. Robust Intelligence was acquired by Cisco, so this is now a Cisco offering rather than an independent vendor.DeepInspect+1
• Mindgard — Focuses specifically on automated AI security testing/red teaming. It's worth investigating if your requirement is an ongoing adversarial-testing pipeline rather than periodic consulting engagements. Industry analyses identify it among the specialist AI-red-team vendors.Failure-First
• Adversa AI — An AI-security specialist with emphasis on adversarial ML and AI red teaming. It is another candidate for testing models against adversarial manipulation rather than relying solely on conventional application security testing.We Compare AI
• Gray Swan — Its Shade product performs automated adversarial red teaming against deployed AI systems. Its primary emphasis is currently generative/agentic AI, so you'd want to verify its support for your particular computer-vision architecture before treating it as a direct fit.Gray Swan+1

For your specific use case

For image recognition, I would make the RFP considerably more specific than simply asking for "AI red teaming." Ask vendors to demonstrate continuous testing for:

• Evasion attacks: adversarial perturbations, patches, stickers, occlusion and transformations designed to cause misclassification.
• Physical-world attacks: whether an attack that works digitally still works when the image comes from a camera under different lighting, angles, distances and resolutions.
• Distribution/adversarial drift: detecting when production inputs increasingly resemble known attack distributions or when model confidence/error characteristics change.
• Model extraction/inference attacks, where applicable.
• Data poisoning and training-set integrity.
• Automated regression testing: every model/version deployment should trigger the adversarial test suite.
• Production telemetry: monitoring should feed results back into the red-team corpus so newly discovered attacks become recurring tests.
• Quantitative metrics: attack success rate, clean accuracy degradation, robustness under transformations, false-positive/false-negative changes, and time-to-detection/remediation.

A useful architecture is continuous runtime monitoring + scheduled automated adversarial generation + CI/CD red-team gates + periodic human-led red-team engagements. That avoids treating an annual red-team report as your entire defense against adversarial drift.

HiddenLayer is particularly aligned with the "continuous" requirement because it explicitly combines professional AI red teaming with continuous attack simulation and runtime security.HiddenLayer+1

If you tell me whether your models are YOLO/object detection, image classification, facial recognition, medical imaging, OCR, or another computer-vision workload, I can narrow this to vendors that specifically demonstrate those attack types and give you an RFP/vendor-evaluation matrix.

Yes. For continuous monitoring of deployed image-recognition models specifically for adversarial drift, I’d distinguish between vendors that provide hands-on AI red-team engagements and those that provide an ongoing automated testing/monitoring platform.

Strong candidates

• hiddenlayer.com — Probably the closest fit to your requirement. Its professional-services team performs AI/ML red teaming, including attacks against predictive/classification models, while its platform supports continuous adversarial testing, attack simulation, model scanning, and runtime security. HiddenLayer explicitly describes testing predictive AI models and tracking vulnerabilities over time.HiddenLayer+2
• grayswan.ai — Offers both automated adversarial testing and private AI red-team engagements. Its service uses human red-teamers against the customer's specific deployment and attack surface. It is currently more oriented toward generative/agentic AI than conventional computer vision, so I'd validate image-model coverage during procurement.Gray Swan+1
• nvidia.com — NVIDIA has an AI Red Team and substantial adversarial-AI expertise, including research around evasion and attacks against AI applications. This is worth considering for a customized enterprise engagement, although it isn't as clearly packaged as a continuous managed service for computer-vision models.NVIDIA+2

For your particular use case

If the concern is "our deployed vision model was robust when validated, but attackers/data distribution are changing and we need to continuously detect new evasion techniques", I'd prioritize HiddenLayer for an initial evaluation.

Your RFP should specifically require:

• Computer-vision adversarial attacks: FGSM, PGD, AutoAttack, CW, spatial/patch attacks, adversarial examples, physical-world attacks, and model-specific black-box attacks.
• Drift detection: distinguish ordinary data/concept drift from security-relevant adversarial drift.
• Continuous testing: scheduled and event-triggered testing after model, data, preprocessing, or infrastructure changes.
• Black-box testing: ability to attack the production inference API without access to model weights.
• Physical-world evaluation if cameras are involved—printed patches, lighting/viewpoint changes, occlusion, etc.
• Regression baselines: track attack success rate, confidence shifts, false-negative/false-positive changes, and robustness against a fixed suite of attacks over time.
• Production monitoring: telemetry that can identify suspicious input distributions without requiring every production image to be retained.
• MITRE ATLAS alignment and reproducible attack reports. HiddenLayer, for example, explicitly describes its attack simulation as aligned with MITRE ATLAS.Amazon Web Services, Inc.

One important distinction: red teaming alone won't necessarily detect adversarial drift continuously. For a production vision system, I'd look for a combination of continuous adversarial evaluation + input/distribution monitoring + runtime anomaly detection + periodic human red-team exercises.

If you tell me whether your models are facial recognition, object detection, OCR, medical imaging, autonomous-vehicle vision, or another type of computer vision, I can narrow this to 5–10 vendors that actually support that model class and compare them on continuous monitoring, black-box attacks, physical-world attacks, deployment model, and likely enterprise fit.

Yes. For deployed image-recognition / computer-vision models, I’d focus on vendors that can do more than one-off LLM jailbreak testing—specifically adversarial examples/evasion, model extraction, poisoning, robustness testing, and continuous regression/drift testing.

Strongest candidates

• Mindgard — Probably the closest match to your requirement. Mindgard explicitly supports automated red teaming of image AI models, including models hosted anywhere, and its platform is designed for continuous testing as models and configurations change. Its services cover reconnaissance, inference, evasion, insider threats, model compromise, and other adversarial techniques.Mindgard+2
• HiddenLayer — A strong enterprise option. Its professional services include AI red teaming, while its platform supports predictive AI as well as generative/agentic AI and provides automated attack simulation, vulnerability tracking, and continuous testing. This makes it particularly interesting if you want red teaming combined with production AI-security monitoring.HiddenLayer+2
• Robust Intelligence — Worth evaluating for continuous ML validation and robustness monitoring. Its RIME technology was built around continuously testing models over time, with stress testing and monitoring of changes in model metrics. Its AI Firewall also uses proprietary algorithmic red teaming to generate security/safety test cases.Robust Intelligence+1
• Mindgard is especially differentiated here because it explicitly advertises image-model support rather than treating computer vision as an afterthought. Its image-model capability is designed to find weaknesses, bias, and misuse scenarios through automated adversarial testing.Mindgard
• Adversa AI — Strong for continuous AI red teaming and managed engagements, although its current public positioning is more heavily oriented toward LLMs/agents than conventional image classifiers. It does offer managed-service red teaming and claims testing on every model update/workflow change.Adversa AI+1

What I'd shortlist for your use case

One important distinction

For your phrase “detect adversarial drift,” I wouldn't buy a conventional annual AI penetration test alone. You want a system that establishes a baseline adversarial robustness score and automatically reruns attacks when:

• a new model/version is deployed;
• training data changes;
• preprocessing changes;
• confidence/calibration shifts;
• the camera/input distribution changes;
• an attacker discovers a new perturbation technique; or
• the model begins behaving differently on previously robust examples.

For a vision model, I'd specifically require testing against FGSM/PGD-style perturbations, spatial transformations, physical-world perturbations, patch attacks, distribution shifts, poisoning/backdoors, model extraction, and confidence/decision-boundary degradation, rather than accepting a vendor's generic “AI red teaming” label.

My first two RFP conversations would be Mindgard and HiddenLayer. Mindgard has the clearest public evidence for automated red teaming of image models, while HiddenLayer looks stronger if you want to combine adversarial testing with a broader enterprise AI-security/production-monitoring platform.Mindgard+1

If you tell me whether your deployed models are YOLO/object detection, image classification, facial recognition, medical imaging, or another CV architecture, I can narrow this to the 3–5 vendors with the most relevant attack coverage and compare their deployment models and likely fit.

Performing continuous security monitoring and automated red teaming for deployed image recognition models (to catch adversarial drift, evasion attacks, and input manipulation) requires specialized Machine Learning Security (MLSec) platforms. While a large portion of the generic "AI Red Teaming" market focuses heavily on Large Language Models (LLMs) and chatbots, a select group of enterprise platforms specifically address predictive, multi-modal, and computer vision neural networks.youtube.com+1[2]blog.qualys.com

The leading specialized companies and platforms providing automated AI red teaming and runtime security monitoring for deployed models include:

• HiddenLayer — Offers an AI Detection and Response (AIDR) platform that delivers non-invasive, real-time runtime security protection against adversarial machine learning attacks (like evasion and data tampering) without needing access to raw underlying data or model code. Their model scanner evaluates supply chain integrity and flags anomalies during inference.hiddenlayer.com+4
• Mindgard — Provides an automated AI red teaming and continuous security platform that tests neural networks across multiple modalities—explicitly supporting computer vision and image/audio models alongside LLMs. It profiles systems for risk, integrates into CI/CD pipelines, and continuously assesses vulnerability to adversarial exploitation.mindgard.ai+2
• Cisco AI Defense (formerly Robust Intelligence) — Extends automated AI validation and production protection. Originally pioneering algorithmic red teaming and AI firewalls, the platform acts as an automated security layer that continuously stress-tests models against adversarial evasion and data poisoning before and during deployment.cisco.com+2
• CalypsoAI (acquired by F5) — Focuses on enterprise AI security, offering automated red teaming frameworks combined with runtime guardrails and policy enforcement. While traditionally utilized for conversational and agentic architectures, its security-testing methodologies map heavily to input validation constraints.giskard.ai+2

If you'd like to narrow this down, let me know:

• Is your image recognition model deployed via cloud APIs, edge devices, or on-premise infrastructure?
• Are you primarily concerned with evasion attacks (adversarial perturbations) or data poisoning/drift over time?

I can help you evaluate which platform architecture fits your deployment pipeline best.

Yes. For deployed image-recognition/computer-vision models, I’d separate the market into vendors that genuinely test adversarial robustness/evasion from vendors whose “AI red teaming” is primarily LLM-focused.

Strong candidates

• miragelabs.dev — Probably the closest fit to your specific requirement. Its platform is explicitly designed for computer vision, audio, and sensor systems, with white-, gray-, and black-box attacks, physical-world realism, and a product called REDLINE for continuous adversarial red-teaming of deployed models.Mirage Labs
• advai.com — Particularly interesting if you want testing + continuous production monitoring. It explicitly supports computer vision, adversarial testing, and monitoring for degradation/drift and emerging failure modes.Advai
• adversa.ai — AI-security/red-team platform with adversarial ML capabilities; independent reviews specifically mention its use for computer-vision projects and adversarial testing.Gartner
• mindgard.ai — Offers AI red-teaming-as-a-service, including evasion and inference attacks, model compromise, and adversarial-ML testing. It combines expert engagements with an ongoing security platform.Mindgard
• deepkeep.ai — Has a dedicated Vision offering alongside adaptive AI red teaming, robustness testing, model scanning, and runtime protection.DeepKeep
• aidxtech.com — Explicitly covers computer-vision robustness and adversarial attack testing, with a broader AI evaluation platform.AIDX
• mottasec.com — More of a consulting/assessment engagement: specifically lists image classification, object detection, facial recognition and medical imaging, including adversarial robustness testing and ongoing-monitoring recommendations.MottaSec
• hiddenlayer.com — A strong enterprise AI-security option. Its red-teaming product supports predictive models as well as LLMs, offers scheduled/continuous testing, and combines attack simulation with runtime protection.HiddenLayer+1

How I'd shortlist them for your use case

For “adversarial drift,” I would prioritize Mirage Labs and Advai first. The distinction is important: ordinary model-drift monitoring tells you that the input distribution or accuracy is changing; it doesn't necessarily tell you that an attacker has discovered a new perturbation, evasion technique, or physical attack that systematically defeats the classifier. Mirage is unusually explicit about continuous adversarial testing of deployed vision models, while Advai combines adversarial testing with continuous monitoring for degradation and emerging failure modes.Advai+1

For an enterprise architecture, I'd look for a service that can continuously run a loop like:

production telemetry → drift/anomaly detection → generate adversarial cases → attack deployed model → measure attack success/robustness → alert → regression test after remediation.

I'd also require testing across digital perturbations (FGSM/PGD, spatial transformations, corruptions, patches), OOD inputs, and—if cameras are involved—physical-world attacks such as printed patches, lighting, angle, distance and occlusion. That last category is where many generic “AI red-team” offerings fall short.

If you tell me whether your models are image classification, object detection, facial recognition, OCR, medical imaging, or video analytics, I can narrow this to the 3–5 best vendors and compare their likely deployment model, testing depth, continuous-monitoring capabilities, and enterprise fit.