Table of Contents

Reach SOC 2 Compliance in 6 Weeks or Less.

  / What Is Penetration Testing? A Practical Guide to Methods, Timelines, and Compliance Outcomes

What Is Penetration Testing? A Practical Guide to Methods, Timelines, and Compliance Outcomes

Cyber threats are no longer theoretical. They are automated, persistent, and increasingly aimed at organisations that believe they are “too small to be a target”.

Whether you are a SaaS startup, a regulated enterprise, or a growing organisation preparing for ISO 27001 or SOC 2, penetration testing is no longer optional. It is a core security and compliance requirement.

At Axipro, penetration testing is designed to do more than find weaknesses. It helps organisations understand their real-world risk, validate security controls, and prioritise remediation in a way that supports audits, certifications, and long-term growth.

Main Objectives of Penetration Testing

The Axipro penetration testing framework is built around four primary objectives:

  • Identify vulnerabilities across applications, infrastructure, and exposed services before attackers do.
  • Improve security posture by understanding how systems behave under real attack conditions, not just theoretical assessments.
  • Prioritise remediation so teams focus on the vulnerabilities that pose genuine business risk, rather than chasing low-impact findings.
  • Validate security controls to ensure that policies, configurations, and safeguards actually work in practice.

Penetration testing is not about producing long reports. It is about producing clarity.

 

Introduction & Methodology

Penetration testing at Axipro follows a structured, repeatable methodology that aligns with modern security standards and compliance frameworks.

The methodology is designed to simulate real-world attacks while remaining controlled, auditable, and business-focused. This ensures findings are both technically accurate and compliance-ready.

The process balances automation with deep manual testing, recognising that tools alone cannot uncover logic flaws, chained vulnerabilities, or contextual risk.

 

Project Map

The project map illustrated above provides a clear, end-to-end view of how an Axipro penetration testing engagement is delivered .

Rather than treating testing as a single activity, Axipro approaches it as a sequence of interconnected phases, each building on the last.

 

Kick Off

The engagement begins with a structured kick-off.

This phase defines:

  • Project stakeholders

  • Scope boundaries

  • Timeline and milestones

  • Terminology and testing methodology

  • Type of testing to be performed

This step is critical. Clear scoping ensures the test reflects real business risk and avoids both blind spots and unnecessary noise.

 

Initial Scanning

Initial scanning focuses on information gathering and attack surface discovery.

Axipro collects intelligence on the target environment using scanning tools and publicly available sources. This mirrors how real attackers begin their reconnaissance.

The goal is not exploitation, but understanding what is visible, reachable, and potentially misconfigured.

 

Assessment & Analysis

 

This is the core analytical phase of the engagement.

During assessment and analysis, Axipro:

  • Scans for known vulnerabilities and misconfigurations

  • Performs automated and manual testing

  • Conducts targeted manual penetration attempts

  • Analyses authentication flows, access controls, and exposed APIs

  • Evaluates real exploitability rather than theoretical risk

This phase separates generic vulnerability scanning from true penetration testing.

 

Exploitation

 

In the exploitation phase, Axipro safely attempts to exploit validated vulnerabilities.

This step answers the most important question for leadership:

What could an attacker actually do with this weakness?

Exploitation is controlled, non-destructive, and focused on demonstrating impact rather than causing disruption.

 

Reporting

The final phase is reporting and closeout.

Axipro delivers a structured penetration testing report that:

  • Documents all findings

  • Rates vulnerabilities by severity

  • Explains business impact in clear language

  • Provides actionable remediation recommendations

The report is designed to support engineering teams, leadership, and auditors alike.

 

Tools Used

Axipro uses a broad range of industry-standard tools, supported by expert-led manual testing .

These include vulnerability scanners, network analysis tools, application testing platforms, API testing tools, and custom scripts.

However, tools are only part of the equation.

Automation finds volume. Expertise finds risk.

Manual testing techniques such as code review, API analysis, SQL injection testing, and custom exploitation scripts are critical to uncovering vulnerabilities that scanners routinely miss.

Black Box Testing

Black box testing is performed with no prior knowledge of the internal workings of the system.

Testers approach the application from an external attacker’s perspective, relying on publicly accessible interfaces and behaviour.

 

Advantages

 

Black box testing provides a realistic simulation of external attacks, helping organisations:

  • Identify externally exposed weaknesses

  • Improve overall security posture

  • Support compliance requirements

  • Prioritise risk based on real-world attack paths

 

Disadvantages

 

Because internal code and architecture are not visible, some deep or logic-based vulnerabilities may remain undetected.

 

White Box Testing

White box testing provides testers with full knowledge of the internal code, architecture, and design.

Axipro’s security team uses this visibility to examine internal logic, security mechanisms, and code quality.

 

Advantages

White box testing enables:

  • Comprehensive testing coverage

  • Identification of complex vulnerabilities

  • Accurate risk assessment

  • Early detection during development

  • Validation of security controls

 

Disadvantages

White box testing can be time-consuming, more costly, and dependent on internal access. It may also create a false sense of security if not paired with external testing.

Grey Box Testing

Grey box testing combines elements of both black box and white box testing.

Testers have partial knowledge of the internal system, such as architecture diagrams or limited access credentials.

 

Advantages

This approach provides a balanced perspective, allowing:

  • Realistic attack simulation

  • In-depth evaluation

  • Efficient vulnerability identification

  • Practical risk prioritisation

 

Disadvantages

Grey box testing may still have scope limitations and incomplete coverage, particularly in complex or legacy environments.

 

Penetration Testing Timeline

While timelines vary based on scope and complexity, a standard engagement includes:

Kick-off and planning, followed by initial scanning, assessment and analysis, exploitation, and reporting.

In most cases, penetration testing is completed within one to two weeks, providing fast, actionable insight without disrupting operations.

Penetration Testing Plans

Axipro offers scalable penetration testing plans aligned with organisational size, growth stage, and compliance needs.

The Basic plan is suitable for smaller organisations or single-framework requirements, including one round of retesting.

The Scale plan supports growing organisations that require multiple retesting cycles and deeper coverage.

The Growth plan is designed for organisations with frequent testing needs and evolving attack surfaces.

Each plan integrates seamlessly with Axipro’s broader compliance services, including ISO 27001, SOC 2, internal audits, and compliance as a service.

Basic

Scale

Growth

Why Penetration Testing Matters Beyond Security

Penetration testing is not just a technical exercise.

It supports:

  • Regulatory compliance

  • Customer trust

  • Enterprise sales cycles

  • Investor due diligence

  • Long-term risk reduction

A clear, well-documented penetration test demonstrates that security is not theoretical, but operational.

Take the Next Step

If you are preparing for certification, responding to customer security questionnaires, or simply want confidence in your security posture, penetration testing is the logical next step.

Axipro combines human expertise, structured methodology, and compliance alignment to deliver penetration testing that actually drives outcomes.

Book a consultation, request a security assessment, or speak with Axipro about integrating penetration testing into your compliance roadmap today.

At Axipro Technology, excellence is standard.

 
 

Axipro Author

Picture of Thatware

Thatware

Blog Highlights

Explore More Articles

One in five organizations has already suffered a breach traced back to shadow AI. Meanwhile, 63% of breached organizations either have no AI governance policy at all or are still drafting one. Below is a complete, copy-ready shadow AI policy template with twelve sections, plus guidance on adapting it for your company size, your industry, and the regulatory frameworks you answer to. The template assumes one hard truth up front: your employees are already using unapproved AI tools. A policy that pretends adoption hasn’t started yet fails on day one, so this one starts from the assumption that it has. What Is a Shadow AI Policy? A shadow AI policy is a formal document that defines how your organization discovers, evaluates, approves, and governs AI tools that employees adopt outside official IT channels. The term borrows from shadow IT, the older problem of unsanctioned software and hardware, but the AI version carries sharper risks: data pasted into a public model may be retained, used for training, or exposed in ways the organization can’t reverse. The policy does three jobs: it separates approved use from unapproved use, gives employees a fast and visible way to request new tools so the sanctioned route beats the workaround, and spells out what happens when someone crosses the line, including how the organization detects it and responds. Shadow AI Policy vs. General AI Acceptable Use Policy Many organizations already have an AI acceptable use policy (AUP) and assume it covers shadow AI. It usually doesn’t. An AUP tells employees how to behave inside approved tools. A shadow AI policy governs the tools themselves: which ones exist in your environment, which ones are allowed, and what happens with the rest. You need both. The AUP handles conduct; the shadow AI policy handles inventory and control. If you only have room for one document, fold the AUP’s data-handling rules into Section 6 of the template below. Let Axipro help you build a business continuity plan that’s practical, compliant, and audit-ready. Strengthen Your Business Continuity Strategy​ Schedule A Consultation The Shadow AI Policy Template (Download Link and Copy-Ready Sections) We’ve created a compliance safe template for Shadow AI Policy, use the link below to create a copy and customize for your company: Download The Shadow AI Policy Template → Copy the sections below into your policy management system and replace the bracketed placeholders. The language is plain on purpose. Legalese gets skimmed. Section 1: Purpose and Scope This policy governs the acquisition, approval, and use of artificial intelligence tools, features, and services at [Company]. It applies to all employees, contractors, interns, and third parties with access to [Company] systems or data. It covers standalone AI applications, AI features embedded in existing software, browser extensions, AI agents, APIs, and personal AI accounts used for work purposes, on both corporate and personal devices. The purpose of this policy is to enable productive AI use while protecting [Company] data, customers, and legal obligations. This policy does not prohibit AI. It prohibits ungoverned AI. That last sentence matters. Employees read the purpose statement first, and it decides whether they see the policy as an enabler or a blocker. Section 2: Definitions and Terminology Shadow AI: any AI tool, feature, agent, or service used for work purposes without formal approval under this policy. Approved AI Tool: an AI tool listed in the Approved AI Tools Registry (Section 4) and used under a [Company]-managed account. Personal AI Account: an account on any AI service registered to a personal email address or paid for personally. AI Feature: AI functionality embedded within otherwise approved software (e.g., an AI assistant added to a project management tool), which requires separate evaluation. Sensitive Data: data classified as [Confidential] or [Restricted] under [Company]‘s data classification policy, including the prohibited data classes in Section 6. Define “AI feature” explicitly. Vendors now ship AI additions into already-approved SaaS products every month, and without this definition, those features inherit approval they never earned. Section 3: Roles and Responsibilities The CISO (or designated security lead) owns this policy, maintains the Approved AI Tools Registry, and runs the approval workflow. Department heads ensure their teams know the policy and surface tool requests rather than suppressing them. Legal and Compliance review tools that touch regulated data or fall under the EU AI Act, GDPR, HIPAA, or client contractual restrictions. IT operates detection and monitoring controls (Section 9). Every employee is responsible for using only approved tools for work, reporting unapproved AI use they discover, and requesting new tools through the workflow in Section 7 rather than adopting them directly. Insider Note: In organizations under roughly 200 people, the “CISO” in this section is often the same overworked IT lead who manages laptops. Name a real person, not a title that doesn’t exist yet. A policy that assigns duties to a phantom role is unenforceable, and auditors notice. Section 4: Approved AI Tools Registry [Company] maintains a registry of approved AI tools at [location/URL]. For each tool, the registry records: tool name and vendor, approved use cases, prohibited use cases, permitted data classes, account type (enterprise/team/individual), data retention and training settings, risk tier (Section 5), approval date, and next review date. Only tools listed in the registry may be used for work. Tools not listed are unapproved by default. The registry is reviewed [quarterly]. Keep the registry somewhere employees actually look, such as your intranet homepage or IT help center, not buried in a GRC platform they can’t access. An invisible registry recreates the problem the policy exists to fix. Section 5: Risk Tier Classification (Low, Medium, High) Each tool in the registry is assigned a risk tier. Low: the tool processes only public or internal non-sensitive data, runs under an enterprise agreement with training opt-out, and produces output that a human reviews before use. Approval by IT Security alone. Medium: the tool processes internal business data or connects to [Company] systems via API or integration. Approval by IT Security plus the data owner. High: the

Legacy threat modeling frameworks such as STRIDE were designed for software that behaves the same way over and over again. Agentic AI does no such thing. It can rewrite its own plan mid-task, call external tools, negotiate with other agents, and produce a different output from identical input. MAESTRO exists because none of the legacy threat modeling frameworks were built to handle that. MAESTRO stands for Multi-Agent Environment, Security, Threat, Risk, and Outcome. It is a seven-layer threat modeling framework created specifically for agentic AI systems, and it has become the closest thing the industry has to a standard method for reasoning about agent security. Understanding MAESTRO in the Context of Agentic AI What MAESTRO Stands For Each word in the acronym carries meaning. Multi-Agent Environment signals that the framework models entire ecosystems of interacting agents, not a single model behind an API. Security, Threat, Risk covers the core discipline: identifying attack surfaces, cataloging threats, and assessing likelihood and impact. Outcome is the part most frameworks skip. MAESTRO asks what an attack actually produces in the real world, because an autonomous agent with tool access turns a compromised prompt into a compromised action. The Origin of MAESTRO (Cloud Security Alliance) The Cloud Security Alliance published MAESTRO in February 2025. Its creator is Ken Huang, Co-Chair of the CSA AI Safety Working Groups and CEO of DistributedApps.ai. The CSA has since applied the framework publicly to real systems, including OpenAI’s Responses API and Google’s A2A protocol, which gives practitioners worked examples rather than just theory. The framework is openly published, and the CSA maintains an official companion tool, the MAESTRO Threat Analyzer, on GitHub. SOC 2, ISO 27001 and HIPAA done for you. Fixed fee, 100% audit pass rate. Audit-ready in 6 weeks. Not 6 months. Schedule Free Assessment Why Traditional Frameworks Fall Short for Agentic AI STRIDE, PASTA, LINDDUN, and OCTAVE all share a founding assumption: the system under analysis follows predictable logic with clearly defined boundaries. You draw the data flow diagram, mark the trust boundaries, and enumerate threats against components that behave deterministically. Agentic AI breaks every part of that assumption. Unique Security Challenges of Autonomous Agents Agents introduce three properties that legacy models cannot express. Non-determinism means the same input can produce different behavior, so you cannot enumerate execution paths in advance. Autonomy means the agent makes decisions and takes actions without a human approving each step, which collapses the usual assumption that a person sits between intent and execution. And in multi-agent systems there is often no stable trust boundary: agents delegate to other agents, consume tool outputs from external servers via protocols like the Model Context Protocol (MCP), and update their own memory and goals at runtime. The Gap Between Legacy Frameworks and Agent-Based Systems The practical consequence is coverage gaps. STRIDE has no category for goal manipulation, where an attacker gradually steers what an agent is trying to achieve. PASTA assumes attacker objectives and data flows are fixed, which fails for systems that learn and adapt during operation. LINDDUN addresses privacy but says nothing about agent collusion or memory poisoning. A threat model built purely on these frameworks will pass review and still miss the attacks that matter most in an agentic deployment. How MAESTRO Addresses Agentic-Specific Risks MAESTRO does not discard the older frameworks. It extends them with a layered reference architecture, an AI-specific threat catalog for each layer, and, critically, explicit analysis of how threats propagate between layers. That cross-layer lens is the framework’s real contribution, because most serious agentic incidents are chains: poisoned data influences a model, the model misleads an agent, and the agent takes an unauthorized action three layers away from where the attack started. The Seven Layers of the MAESTRO Framework MAESTRO decomposes any agentic system into seven layers, each with its own threat landscape. Layer 1: Foundation Models The core LLMs or other models the agents reason with. Threats here include adversarial examples, model extraction, backdoored weights, and jailbreaks that bypass safety training. If the model is a third-party API, supply chain risk lives at this layer too. Layer 2: Data Operations Everything the agent ingests, stores, and retrieves: training data, RAG pipelines, vector databases, and agent memory. Data poisoning and memory tampering are the signature threats at this layer, and they are especially dangerous because a poisoned memory persists across sessions and keeps shaping future decisions long after the initial attack. Layer 3: Agent Frameworks The orchestration software that turns a model into an agent: LangChain, CrewAI, AutoGen, custom planners, and tool-calling logic. Threats include prompt injection through tool outputs, insecure tool definitions, and manipulation of the planning loop itself. Layer 4: Deployment Infrastructure The servers, containers, and cloud services the agents run on. The CSA’s threat catalog here reads like traditional cloud security with an agentic twist: compromised container images carrying malicious agent code, Kubernetes orchestration attacks, denial of service against agent runtimes, and tampering with Infrastructure-as-Code templates that provision agent resources. Layer 5: Evaluation and Observability The systems that monitor, evaluate, and debug agent behavior. This layer is often forgotten, and attackers know it. The CSA specifically flags poisoning observability data: manipulating the telemetry fed to monitoring systems so that incidents stay hidden from security teams while malicious activity continues. Layer 6: Security and Compliance MAESTRO treats this as a vertical layer that cuts across all others: identity and access management, guardrails, policy enforcement, and compliance controls. Threats include permission escalation, guardrail bypass, and compromise of the security agents themselves in architectures where AI enforces policy on other AI. Layer 7: Agent Ecosystem The environment where agents interact with users, other agents, and marketplaces. This is where the genuinely novel threats live: agent impersonation, misleading agent capability cards, tool squatting, and collusion between agents to achieve outcomes no single agent was authorized to pursue. Insider Note: In real assessments, Layers 5 and 6 expose the maturity gap fastest. Most teams’ shipping agents can describe their model and their orchestration framework in detail, then

EU AI Act Hiring Map

AXIPRO STUDY New Study: Europe is hiring AI builders faster than AI governance professionals Axipro analyzed 3,519 AI-related job postings across eight EU countries. For every professional hired to keep AI lawful, safe and accountable, nearly seven were hired to build more of it, and the gap is widest exactly where you’d least expect. Take EU AI ACT READINESS QUIZZ 16 AI Builders : 1 AI Governors Sweden — Europe’s widest AI governance gap 3,519 Job Postings Analyzed 8 EU Countries 2 Role Categories: Builders vs Governors July 2026 Date of Job Postings Analyzed The findings Finding 1: Sweden hires 16 AI builders for every 1 person to govern them Throughout our data-set we found the same pattern across all eight countries: the more a nation hires to build AI, the less it hires to govern it. France runs eleven builders to every governor. Even Ireland, the most balanced in Europe, looks responsible mainly because the US tech giants headquartered there import global-governance discipline under overlapping DORA and AI Act pressure.  3.5→16 builders hired per governor, Europe’s most balanced country to its least. Ireland 3.5 Germany 5.7 Spain 6.0 Italy 7.1 Netherlands 7.2 Belgium 7.9 France 11.4 Sweden 16:1 0 4 8 12 16 Builders hired per AI governor Source: Axipro, 2026 Sweden has one of the strongest engineering cultures in Europe. It also carries the widest governance gap we measured: sixteen AI builders hired for every person hired to govern them. France sits close behind at eleven to one. The most balanced country, Ireland at 3.5 to one, looks responsible for a reason that has little to do with virtue. The US tech giants headquartered in Dublin import global governance discipline, and they do it under the combined weight of the AI Act and DORA, the EU financial-sector resilience regime in force since January 2025. Engineering strength does nothing to close a governance gap, and it may widen it. A country that ships AI faster produces more systems that fall under the Act’s scope and, on this evidence, fewer people positioned to document, monitor, and defend them. Being good at building AI offers no protection against governing it badly. The countries most confident in their technical talent are running the largest deficit against the law. Explore AI governance hiring by country Click any country to see how many AI builders it hires for every governance professional, and where it ranks against the rest of Europe. Germany — 5.7 builders per governorDE France — 11.4 builders per governorFR Spain — 6.0 builders per governorES Italy — 7.1 builders per governorIT Netherlands — 7.2 builders per governorNL Belgium — 7.9 builders per governorBE Ireland — 3.5 builders per governorIE Sweden — 16 builders per governorSE 3.5 — balanced 16 — widest gap Source: Axipro, 2026 Sweden 16builders for every governance professional Rank 1 of 8 · 20 governance roles vs 319 builder roles posted Only 30% of the AI governance roles name the AI Act Share this Embed this map Copy & paste — links back to Axipro Copy embed code Branded, one paste, backlink included. × Share this country insight Share this AI governance gap X / Twitter LinkedIn Facebook WhatsApp Bluesky Email Copy link Choose a platform or copy the link. A view of the same country-level dataset behind the interactive map: governance roles, builder roles, builder-to-governance ratio, and the share of governance postings that name the EU AI Act. AI governance jobs Europe statistics by country: governance roles, builder roles, builder-to-governance ratio and AI Act mention percentage. Country Governance roles Builder roles Builder-to-governance ratio AI Act mention % Sweden 20 319 16.0:1 30.0% France 39 443 11.4:1 38.5% Belgium 38 299 7.9:1 39.5% Netherlands 61 439 7.2:1 31.1% Italy 40 284 7.1:1 45.0% Spain 64 384 6.0:1 28.1% Germany 88 501 5.7:1 27.3% Ireland 96 335 3.5:1 14.6% Source: Axipro analysis of AI builder, governance and compliance job postings across eight European countries. “AI Act mention %” is the share of governance postings that explicitly name the EU AI Act. Finding 2: The law nobody names. Most AI governance jobs still do not mention the EU AI Act Europe spent years drafting the AI Act. It cleared the European Parliament, survived the Digital Omnibus revisions, and now carries penalties that reach €35 million or 7% of global turnover for the most serious breaches, a ceiling that makes GDPR fines look modest. Yet fewer than three in ten of the governance roles created to handle it actually name the law in the job description. Among builder roles, the figure collapses to one in twenty-five. More than 7 in 10 Governance job descriptions do not mention the EU AI Act. This number rises to 9 in 10 for all AI job descriptions. Despite hiring for governance, risk, privacy, and compliance roles, most employers are not yet translating the EU AI Act into explicit job requirements. That disconnect should stop you. The people being hired to make Europe compliant are, for the most part, not being hired against the Act by name. They are titled around adjacent ideas: risk, ethics, model validation, data protection. Some of that work will map onto the Act’s requirements. Much of it will not, because a role written without the regulation in view rarely produces the conformity assessments, technical documentation, and human-oversight structures the Act specifically demands. Readiness is even thinner than the headcount suggests. Simply counting governance hires overstates how many people are actually working the law. What job descriptions actually name The EU AI Act is visible in governance roles — but still absent from most job ads. Across the laws and frameworks most relevant to AI governance hiring, the EU AI Act appears in fewer than three in ten governance postings, and only 4% of builder postings. Law or framework Governance roles naming it Builder roles naming it All roles naming it Governance mentions EU AI Act 28.5% 4.0% 7.6% 127 GDPR 26.9% 5.7% 9.6% 120 ISO 27001 11.4% 1.3% 2.8% 51