Identify vulnerabilities in AI systems and define a clear security roadmap.

Ancore helps secure machine learning pipelines, generative models, and inference engines by detecting anomalies, hardening systems, and supporting compliance.

How Ancore
AI Security Review
Strengthens Your Business

Ancore's AI Security fortifies your digital infrastructure against evolving threats. Our advanced AI-driven systems detect anomalies, predict attacks and automate responses in real time. Deploy machine learning algorithms that continuously monitor networks, endpoints and data flows, neutralizing vulnerabilities before exploitation. This proactive defense integrates seamlessly with existing security stacks, ensuring compliance, minimizing downtime and empowering your team to focus on growth.

Neutralize Threats in Real Time
AI-powered detection that identifies and mitigates cyber risks instantly, preventing breaches and data loss.
Predict and Pre-empt Attacks
Predictive analytics that forecast potential vulnerabilities, enabling proactive hardening of your defenses.
Achieve Compliance Assurance
Automated audit trails and reporting that streamline regulatory adherence and reduce audit preparation time.

Our Methodology

  • Document current AI systems, their purposes and strategic importance. Identify all machine learning pipelines, generative models and inference engines in production or development. Understand business objectives, risk tolerance and compliance requirements that will shape your security approach.

  • Map the complete AI ecosystem—data sources, training pipelines, model architectures, deployment environments and integration points. Identify who has access to models and data, how systems are monitored and what controls currently exist. This creates a comprehensive inventory of assets requiring protection.

  • Assess exposure to adversarial attacks, model poisoning, data manipulation, prompt injection and other AI-specific threats. Test models for robustness against evasion techniques. Evaluate data integrity controls, training pipeline security and inference endpoint protections. Document existing gaps and potential attack vectors.

  • Determine the business consequences of different attack scenarios—from degraded model performance to compromised decision-making or regulatory breaches. Prioritise vulnerabilities based on likelihood, impact and exploitability. Identify quick wins alongside critical risks requiring immediate attention.

  • Design a phased programme to harden algorithms, implement anomaly detection, strengthen data governance and establish monitoring capabilities. Define specific initiatives, resource requirements, timelines and success metrics. Align security investments with business priorities and regulatory obligations.

  • Deploy technical controls - adversarial training, input validation, model versioning, access controls and continuous monitoring. Establish governance frameworks for model development, testing and deployment. Create incident response procedures for AI-specific threats. Build ongoing capability to detect, respond to and learn from security events.

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How it works

Discovery

We establish your AI baseline by documenting all AI systems, machine learning pipelines, generative models, and inference engines. Data sources, model architectures, deployment environments, access controls, and integration points are mapped.

Output: Complete AI asset inventory, current security posture report, baseline assessment.

Assessment

We identify threats and vulnerabilities specific to your AI systems—testing for adversarial attacks, model poisoning, data manipulation, and prompt injection. Vulnerabilities are prioritised by likelihood, impact, and exploitability, with critical risks flagged immediately.

Output: Threat assessment report, quick wins, critical risk flags, business impact analysis.

Planning

We develop your AI security roadmap designing a phased programme to harden algorithms, implement anomaly detection, and strengthen data governance. Specific initiatives with resource requirements, timelines, and success metrics are defined and aligned to regulatory obligations.

Output: AI security roadmap, phased programme plan, resource requirements, success metrics.

Implementation

We deploy initial controls and establish governance frameworks. Technical protections including adversarial training, input validation, and model versioning are implemented. Incident response procedures and continuous monitoring capabilities are set up and handed over.

Output: Functioning security foundation, documented procedures, deployed controls, ongoing threat detection capability.

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Benefits of Ancore’s AI security review service

Early Risk Detection

Identify vulnerabilities in AI models, pipelines, and inference systems before they lead to security failures or misuse.

Stronger Model Integrity

Reduce exposure to model poisoning, adversarial attacks, and data manipulation that can weaken AI system performance.

Better Compliance Readiness

Strengthen governance, controls, and documentation to support internal policies and evolving regulatory requirements.

Greater Trust in AI Systems

Build confidence among stakeholders by showing that AI systems are reviewed for security, resilience, and responsible use.

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Not sure where to start?

Take a quick AI security assessment to see if going fractional is the right move.

It takes only 5 minutes to get to know where you stand in terms of your AI security. Get to know your gaps and how to optimize them.

Take Assessment
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Frequently Asked Questions

  • An AI security review is a structured assessment of the security risks specific to artificial intelligence systems - including machine learning pipelines, generative models, and inference engines. It evaluates exposure to adversarial attacks, model poisoning, data manipulation, and prompt injection, then produces a prioritised roadmap for hardening algorithms, strengthening data governance, and establishing ongoing monitoring. Ancore delivers this as a phased programme with deployed controls and governance frameworks, not just a report.

  • Ancore delivers three core outcomes: real-time threat neutralisation through AI-powered detection that identifies and mitigates cyber risks targeting AI systems; predictive attack analytics that forecast potential vulnerabilities and enable proactive hardening; and compliance assurance with automated audit trails and reporting that streamline regulatory adherence and reduce audit preparation time.

  • Ancore tests for adversarial attacks that manipulate model inputs to produce incorrect outputs, model poisoning that corrupts training data to degrade model performance, data manipulation that undermines the integrity of datasets used for training and inference, and prompt injection that exploits generative models to bypass safety controls or extract sensitive information. Models are evaluated for robustness against evasion techniques, and data integrity controls are assessed across the entire pipeline.

  • Model poisoning is an attack where an adversary introduces corrupted or malicious data into a model's training pipeline, causing the model to learn incorrect patterns and make flawed decisions in production. It matters because a poisoned model can appear to function normally while producing systematically biased or manipulated outputs, making it difficult to detect without targeted testing. Ancore tests for poisoning as part of the threat assessment and designs training pipeline controls to prevent it.

  • This service is best suited for CTOs, CISOs, and engineering leaders at organisations that are deploying machine learning models, generative AI, or automated decision-making systems and need to understand and mitigate the security risks specific to those systems. It's also valuable for organisations subject to emerging AI regulation or those presenting AI governance posture to boards, investors, or enterprise customers.

  • Yes. Ancore aligns the security roadmap to regulatory obligations and emerging AI governance frameworks. Automated audit trails and documentation are established so that compliance evidence is generated continuously rather than assembled ad hoc before audits. This positions organisations to meet evolving requirements around AI transparency, safety, and accountability as regulation matures.