AI-Specific Threat Modeling
Identifying attack paths particular to AI systems, including injection through clinical content, tool exploitation, and extraction of information the model has access to.
Custom Software
Hire Healthcare AI Security Engineers
Healthcare AI security engineers secure the systems built around models: the endpoints that serve them, the data that reaches them, the tools they can invoke, and the supply chain they depend on. They threat model AI-specific attack paths and build controls, since AI systems create exposure conventional application security does not address.
The new surfaces are specific. Content entering a context window can alter behavior. Tool access can be exploited to reach systems the model should not touch. Prompt and output logs accumulate clinical data in engineering platforms. Model artifacts and dependencies form a supply chain. Conventional application security covers none of this directly. Our hire dedicated developers hub covers adjacent roles.
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What AI Security Engineers Build
The work spans threat modeling, control implementation, and testing across surfaces that did not exist in the organization’s previous architecture. The work below reflects that. Supply chain appears among them because models, embeddings, and framework dependencies arrive from external sources and are frequently adopted with less scrutiny than conventional software components.
Injection Defense Implementation
Building input handling and context isolation, since clinical text can contain content resembling instructions without any adversarial intent behind it.
Tool and Agent Access Controls
Constraining what models can invoke and reach, with scoped credentials and approval gates, since tool access is the path from output to real-world effect.
Model Supply Chain Assessment
Evaluating provenance of models, embeddings, and framework dependencies, since these are adopted from external sources with less review than conventional components.
Log and Telemetry Exposure Reduction
Addressing prompt and output logs that accumulate clinical content in engineering platforms, which is a common and overlooked concentration of PHI.
AI Incident Response Preparation
Building the investigation capability to determine what a model was asked, what it accessed, and what it returned when a security question arises.
Healthcare Context This Role Requires
AI security in healthcare combines new attack surfaces with existing PHI obligations. An extraction attack against a clinical AI system is a disclosure incident. A tool exploit reaching an EHR is unauthorized access. The consequences map onto established categories even though the paths are new. The context below spans the healthcare work you assign.
01
Injection Arrives Through Clinical Content
Record text can contain content that behaves like instructions without anyone intending it. This is an ordinary property of clinical documentation rather than an attack.
02
Tool Access Converts Output Into Effect
A model that can only produce text is limited. One that can invoke tools can act, which makes tool scoping the primary control between output and consequence.
03
Logs Concentrate PHI in Engineering Platforms
Prompt and output logging accumulates clinical content in observability systems governed for engineering rather than as clinical records.
04
Extraction Attacks Are Disclosure Incidents
Successfully extracting information a model has access to is unauthorized disclosure under existing obligations, regardless of the novelty of the method.
05
Supply Chain Scrutiny Lags Conventional Software
Models and embeddings are downloaded and adopted with less review than libraries receive, despite carrying comparable or greater risk.
06
AI Security Does Not Replace Application Security
The systems around models remain ordinary applications with ordinary vulnerabilities. AI-specific work adds surfaces rather than substituting for existing practice.
Technical Skills This Work Requires
This is security engineering applied to a new architecture, combining conventional application security with understanding of how AI systems fail. The differentiating skill is knowing which controls actually constrain behavior versus which merely discourage it. The competencies below reflect that. Weight enforcement design and testing above familiarity with published attack taxonomies.
Threat Modeling for AI Architectures
Mapping data flows, trust boundaries, and tool access across AI systems, identifying where untrusted content reaches contexts that influence behavior.
Context Isolation and Input Handling
Separating instruction from data where possible, validating content entering context windows, and constraining what retrieved material can influence.
Tool Permission Architecture
Designing scoped credentials, approval gates, and reachability limits for tool-using systems. Our healthcare integration work covers the systems those tools reach.
Adversarial Testing of AI Systems
Probing whether constraints hold under crafted input, since controls untested against deliberate attempts provide assurance rather than protection.
Logging Architecture With Exposure Limits
Designing telemetry that supports investigation without accumulating clinical content in platforms governed to engineering rather than clinical standards.
Supply Chain and Dependency Review
Assessing model and dependency provenance, including what a downloaded artifact contains and whether its source can be verified.
How We Evaluate AI Security Engineers
The distinguishing question is what they successfully attacked. Engineers who tested their own systems adversarially found gaps; those who implemented published controls without probing them have assurance rather than evidence. Our assessment centers on testing practice and enforcement design. Our delivery process includes review points where you can reassess fit.
Successful Attacks They Executed
We ask what they got through in their own systems. Engineers who never probed adversarially have controls whose effectiveness nobody established.
Tool Access Scoping
We ask how tool permissions were constrained. Broad credentials mean a single exploited path reaches everything the system can technically touch.
Log Exposure Assessment
We ask what clinical content their logs contained. Engineers who never audited telemetry have PHI accumulating in platforms nobody governs as clinical.
Injection Handling Approach
We ask how they treated retrieved clinical content. Engineers assuming record text is trusted input have not considered how it can influence behavior.
Supply Chain Practice
We ask how models and dependencies were vetted. Artifacts adopted without provenance review carry risk comparable to unreviewed third-party code.
Verified Security Experience
We describe which AI systems each engineer secured and what testing they performed. We do not claim security certifications for engineers who do not hold them.
Engagement Options for AI Security Work
Engagements should start with threat modeling and adversarial testing of what already exists, because most organizations have deployed AI without either. Structures below reflect that. We also note that AI security belongs within security engineering rather than as a separate function for most organizations.
Threat Model and Adversarial Assessment
Mapping AI attack surfaces and testing deployed systems against them. This regularly finds tool scoping and log exposure issues nobody had examined.
Control Implementation Engagement
Building injection defenses, tool permission architecture, and logging controls where assessment identified gaps requiring engineering rather than policy.
Security Engineer Alongside AI Development
Embedding security review in AI delivery catches architecture decisions before they become findings, which is cheaper than remediating after deployment.
Augmenting Your Security Team
Where you own security practice, staff augmentation adds AI-specific expertise within your existing threat modeling and testing standards.
Full Team With Security Included
A dedicated healthcare development team treats AI security as part of delivery rather than as a separate assessment after the fact.
Fixed-Scope Assessment Deliverable
Where the systems are defined, a fixed-scope assessment under our engagement models delivers threat model, testing findings, and prioritized recommendations.
Tell Us What Your AI Systems Can Reach
Share your deployed AI capabilities, what tools and data they access, and what logging exists. Reachability and log exposure are where assessment usually finds most.
Testing Authorization, Disclosure, and Boundaries
Security testing of clinical systems requires authorization and careful handling. We build to HIPAA-aligned practices where HIPAA applies; software cannot be HIPAA certified. We do not perform testing without documented authorization, and findings involving actual disclosure are reported to your privacy function immediately rather than only in a final report.
01
Testing Under Documented Authorization
Adversarial testing occurs in agreed environments with documented authorization, since the same activity is indistinguishable from an attack without it.
02
Disclosure Findings Reported Immediately
Where testing reveals actual unauthorized access to clinical data, your privacy and security functions are informed immediately rather than at report delivery.
03
Controls Enforced in Code, Not Instruction
Security constraints are implemented as enforcement rather than as model instructions, since instructions are advisory to a probabilistic system.
04
Log Exposure Treated as a Finding
Clinical content accumulating in engineering platforms is reported as an exposure requiring remediation rather than accepted as an operational convenience.
05
Sensitive System Testing Care
Testing systems touching behavioral health requires additional care. We built CHIPSS, a behavioral health system, where access constraints were foundational.
06
Work We Would Not Perform
We would not test production clinical systems without authorization, exfiltrate real patient data to demonstrate a finding, or present assessment as certification of security.
Cost to Hire Engineers and Secure AI Systems
Cost concentrates in threat modeling, testing, and control implementation rather than tooling. Remediation cost depends on what assessment finds, and log exposure remediation is frequently larger than expected because it touches retention and platform governance. We publish no figures on risk reduction. What we deliver is documented findings and implemented controls.
MVP or Single Module
$40,000 to $80,000
Threat model and adversarial assessment of one AI capability with prioritized findings and implementation of the highest-priority controls.
Full Platform Build
$80,000 to $200,000
Security across an AI portfolio with threat modeling, tool permission architecture, injection defenses, logging remediation, and incident response capability.
Enterprise Deployment
Starting at $200,000
Multi-facility AI security with governance integration, testing programs, control implementation, and incident response across several clinical environments.
Discovery Phase Scoping
Discovery is paid and time-boxed. It produces an AI attack surface map, adversarial testing findings, prioritized remediation recommendations, and an itemized fixed-scope estimate.
Cost Drivers to Expect
Capability count, tool and data reachability, logging platform state, testing depth, remediation scope, identity infrastructure complexity, and governance documentation requirements.
Ongoing Support Costs
Attack techniques and model behavior change. Budget for periodic retesting, control review after model updates, log governance maintenance, and incident response readiness.
Third-party licensing, cloud infrastructure, data subscriptions, and hardware are separate from engineering cost and itemised clearly.
Why Secure AI Systems With Taction
Two questions matter. Whether the vendor tests adversarially rather than implementing published controls, and whether they audit log exposure. Taction Software has built healthcare software since 2013, more than twelve years, with over 200 healthcare projects delivered and ISO 27001 certification. Leadership brings more than twenty years of personal experience in the field, which is separate from company age. Our wider case for Taction sits elsewhere.
We Build the Systems We Assess
We build clinical AI, which means we know where controls are typically weak. Our healthcare case studies reflect both sides of that work.
Clinical Access Control Experience
We built Voyant Health, an EHR platform, and CHIPSS, a behavioral health system. Understanding how these enforce permissions determines what tool access should respect.
ISO 27001 Certified Security Management
Taction Software holds ISO 27001 certification covering our information security management practices. It certifies our internal processes and does not certify your AI systems.
We Test Rather Than Assert
Controls are probed adversarially before we describe them as effective, which produces findings organizations sometimes did not expect and can act on.
We Will Report Log Exposure First
Prompt and output logs holding clinical content in engineering platforms is the finding we encounter most and the one organizations least anticipate.
We Will Say This Belongs in Security Engineering
Most organizations need AI expertise within their security function rather than a separate role. That recommendation reduces the engagement to focused assessment.
FAQs
Frequently Asked Questions
We map what your AI systems can reach, review logging and tool access, then present engineers with AI security experience. You interview and approve each placement.
Assessment and remediation for one capability runs $40,000 to $80,000, portfolio security $80,000 to $200,000, and enterprise programs start at $200,000. Tooling and cloud are itemized separately.
Our delivery history includes the Voyant Health EHR platform, the CHIPSS behavioral health system, and the FDA-registered applications Revive Ease and PainKare, within more than 200 healthcare projects delivered since 2013.
Clinical content accumulating in prompt and output logs held in engineering observability platforms, followed by tool access scoped more broadly than any use case requires.
No. Defenses reduce it substantially through context isolation, input handling, and constraining what tool access can achieve, but no control eliminates the surface completely.
Guardrails constrain model output in real time. Security work addresses the surrounding architecture: tool reachability, injection paths, supply chain, logging exposure, and incident response.
Share your deployed AI capabilities, their tool and data access, your logging arrangements, your identity infrastructure, and the engagement model you have in mind. We will test adversarially under documented authorization and report exposure findings immediately. We do not certify security.