Securing the Agentic Enterprise: Practical Steps for Security Leaders

AI-assisted development has changed the application security equation; here's how organisations can respond and where to start.

 TL;DR AI agents are now active participants in software development. The security controls that worked for human developers; periodic penetration testing, manual code reviews, point-in-time assessments do not scale to this environment. Here we set out the practical steps security leaders should take now and how CyberOne and Microsoft can support the journey.

Software development has changed. Developers are using AI-powered coding assistants, autonomous agents and foundation models to accelerate delivery. Microsoft describes this as a shift towards intent-first development: developers guide and supervise intelligent systems rather than manually authoring every line of code.

The security implications are equally real and many organisations are only beginning to work through them. Larger volumes of code, produced faster, sometimes with vulnerabilities embedded by the AI that generated it. Traditional application security controls were not designed for this pace or this attack surface.  

The New Risk Surface

The risks that come with AI-assisted development are distinct from those that traditional application security was built to address. Security leaders need to account for:

• AI-generated code containing exploitable vulnerabilities - produced at speed, at scale, without the friction that historically slowed human developers
• Prompt injection attacks - malicious inputs designed to manipulate AI agents into executing unintended actions
• Software supply chain compromise introduced through AI tooling and third-party model dependencies
• Agent privilege escalation - where an AI agent gains access beyond its intended scope, often through misconfigured permissions or inadequate least-privilege controls
• Model manipulation and poisoning during training or inference - affecting the integrity of AI outputs across the business
• Shadow AI development - teams using AI coding tools outside sanctioned processes, creating undiscovered risk
• Unauthorised access to sensitive data processed or accessed by AI agents during development workflows

Each of these requires a different control response. Together they demand a security posture that is continuous, embedded into development workflows and capable of operating at the speed AI development runs.

From Periodic Testing to Continuous Discovery

Scheduled penetration tests and manual code reviews remain valuable. They will not go away. But they cannot be the primary mechanism for managing application security risk in an AI-assisted development environment.

Microsoft's MDASH (Multi-Model Agentic Scanning Harness) addresses this directly. Rather than a point-in-time assessment, MDASH runs continuously throughout the software development lifecycle, identifying vulnerabilities earlier and integrating findings into the tools developers already use.

The Microsoft Defender integration, now available in expanded preview, enriches vulnerability findings with real production signals like internet exposure and data sensitivity so that teams prioritise what actually matters rather than working through theoretical risk. AI-assisted remediation through GitHub Copilot Autofix reduces the burden on development teams further.

For organisations pursuing DevSecOps maturity, continuous vulnerability discovery through MDASH creates measurable outcomes:

• Remediation cost reduction: finding and fixing vulnerabilities earlier in the development cycle costs a fraction of post-production remediation
• Smaller attack surface in production: confirmed, exploitable findings filtered from theoretical noise
• Developer productivity: AI-assisted remediation within existing workflows, not bolted-on security overhead
• Stronger risk management posture: continuous visibility rather than periodic snapshots

Governing AI Agents at Scale

Beyond application security, the rise of AI agents as active participants in development introduces a governance challenge that security leaders are only beginning to map: managing non-human identities at scale.

AI agents can analyse requirements, generate code, interact with APIs, access repositories, execute workflows and support deployment activities. In many cases they operate with levels of access that would prompt scrutiny if a human developer requested the same permissions. The governance frameworks most organisations have built were designed for human workers.

The controls that matter for AI agent governance mirror those organisations already apply to human access:

• Strong identity controls for every agent deployed unique, auditable identities rather than shared service accounts
• Least-privilege access policies that limit what each agent can reach, reviewed and enforced continuously
• Continuous monitoring of agent activity across development and operational environments
• Full activity auditing for traceability, compliance and incident investigation
• Governance frameworks that define accountability for agent behaviour, escalation paths and response procedures

Microsoft Entra, Microsoft Defender and Microsoft Intune now work together to provide the visibility, runtime protections and governance controls needed to manage agent risk. Microsoft Purview adds data security posture management and runtime data loss prevention for agent prompts, preventing sensitive data from reaching AI models during development.

CyberOne can help organisations design and implement these controls as agent adoption accelerates, drawing on both our Microsoft Security expertise and our offensive security capability to test and validate the controls in practice.

Where to Start

Security leaders do not need to solve this all at once, a practical starting point covers three areas:

1. Assess your current application security posture against an AI-assisted development environment: most existing programmes have significant gaps
2. Map your AI agent inventory: understand what agents are running, what they have access to and how they are being governed today
3. Evaluate your DevSecOps maturity: identify where continuous vulnerability discovery can replace or augment existing point-in-time controls

CyberOne offers structured assessments across all three areas, working within Microsoft environments and aligned to Microsoft's security frameworks. As an MDASH Engaged Partner, we can also provide early access context on how MDASH will integrate into your existing security operations.