Category: Security

Application Security, Web Security, Data Security, Cloud Security

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Agentforce: The New AI Wave

Last month, I attended Dreamforce 2024, the world’s largest software conference, in San Francisco. This massive annual event is always a great learning experience. Dreamforce’s 2024 key announcement was a New AI Era with Agentforce.

Agentforce is synonymous with AI Agent. As I explained in my previous blog about AI agents, I will explain Agentforce in the context of Salesforce/MuleSoft.

The study found that 90% of businesses say that their industry has become more competitive in the last three years, and 48% say it has become much more competitive. This led to decreased margins, force to more productivity, and transformed businesses to remain relevant in the market for any industry.

So the question is, how do we close this gap and become relevant to the market for any industry?

We started the AI journey with Predictive analytics as the first wave of AI. Next, we move into the Generative AI wave. Now we are next inflection point as Agentforce or AI agent. So AI Agent is waiting for us to ultimately close this gap and of course, the way that we’re going to do this is to get more time back, more productivity, and have more business growth with AI agents.

agentforce

So here are a few queries, I am trying to explain

What is Agentforce?

The newest Salesforce tool allows customers to build and customize autonomous agents to scale their workforce. It is a UX for customers to leverage with their data sources to deliver more human-like interactions.

How does Agentforce help customers achieve business goals?

Agentforce gives companies a 24/7 agent to engage on their behalf to resolve sales, service, and marketing-related.

topics including customer service cases and prospect engagement.

With Agentforce, companies can drive productivity to deliver higher profitability, while building stronger customer relationships.

How does MuleSoft enhance Agentforce?

Salesforce primarily focuses on the front end “human assistant” type of agents with the Agentforce UX, while MuleSoft primarily focuses on back-end domain expert agents who manage domain complexity (inventory, payroll.) and power other prompts or agents.

MuleSoft expands the actionability of the Agentforce agent by providing API actions and other domain assets for

broader context to the role, knowledge, actions, guardrails, and channel.

How are customers accessing data for Agentforce?

The Agentforce messaging encourages customers to use Data Cloud to bring in their data and ground Agentforce. To add MuleSoft into this conversation, leverage our value prop for MuleSoft + Data Cloud; where MuleSoft accelerates value against four use cases (on-premises, transactional, unstructured, activation):

● On-premise data: MuleSoft can run locally and stream data to Data Cloud, giving Agentforce additional context for improved grounding and better decision making.

● Transactional data: Transactional systems will want queuing, error handling, and delivery controls for ingestion

— functionality MuleSoft can easily deliver so that Agentforce agents aren’t slowed down.

● Unstructured data: MuleSoft offers pre-built accelerators for unstructured data ingestion to Google Drive,

Confluence, and SharePoint as well as OCR for images. Agentforce agents can have immediate access to data

from scanned images like government identification.

● Activation: Use MuleSoft to respond to data events in Data Cloud and drive action in real time to any downstream system for full circle updates.

What is the agent use cases that MuleSoft supports?

● Service Agents: Agentforce needs contextual data from external systems in order to deflect cases faster

● Sales Agents: MuleSoft can upload, and share leads from and with partners without compromising data integrity, securely with your governance rules. Near real-time synchronization with external systems ensures that Agentforce can engage with prospects starting at the moment leads come in.

● Commerce Agents: Setting up and managing storefronts requires data from external systems including product information, inventory levels, and pending vendor shipments. MuleSoft connects to external systems for near real-time updates so Agentforce can respond with accurate information.

● Employee Service Agents (Workday): Automating onboarding and provisioning for new hires requires data from external systems, and in some cases is unstructured data found in pdf, jpg, and png files like scanned government I.D.s and manually filled out forms. MuleSoft’s Intelligent Document Processing makes it easier to upload unstructured data so that you can share it faster with Agentforce.

How is Agentforce different from the MuleSoft AI Chain (MAC) Project?

MAC Project mainly targets a technical person, i.e. MuleSoft users and developers. With the MAC Project, customers can create powerful agents, fully composed in the MuleSoft Anypoint Platform and benefit from its End-to-End Lifecycle Governance and Management capabilities. With API Management, you can sprinkle it on top of LLM specific policies, to further implement the security aspects when interacting with LLMs. MAC Project is an open source project, which is currently being productized. Agentforce is more for non-technical users who wants to build powerful agents directly in Salesforce. It is fully integrated into every Salesforce Cloud and provides out-of-the-box integration to the Salesforce ecosystem.

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AI-Powered Experiences
Connect | Automate | Scale

Over the last few years, Generative AI has played a significant role across organizations. It is also very  interesting that just 2% expect few to no barriers to bringing Generative AI into their organization.

In IT, change is the only constant. We migrated to the cloud, we’re managing an explosion of customer data, and we’re starting to automate our processes. We expect this AI inflection point more nervous than other big waves of innovation. To manage these inflection points it is very important to streamline our AI journey.

Our first priority is to unlock our data and make it discoverable. We need to create new experiences to unlock your data, from anywhere, and to make it discoverable. This includes on-premises, hybrid/cloud data, as well as data in any format, including structured and unstructured data. Integration/APIs help you to build a framework to unlock data across all of your disparate systems.

Since data is everywhere and sources are spread across your organization. It is a human-centric task. To mitigate these human-centric tasks we need to create workflows & automate manual tasks across structured and unstructured data with minimal coding. This can be achieved by leveraging APIs, data cloud, and automation tools like RPA and IDP.

Next, we talked about the importance of building securely. With a backlog of ongoing projects, we need a way to scale the use of these API building blocks across the business, with security and governance. We need a way to protect and implement security policies across every API in your digital space before you launch your next application, like an e-commerce platform or even a mobile app. Universal API Management allows us to bring security and governance to any API.

And finally, we need just one more piece – an AI model. AI model interacts with LLMs via an API. As we make our inventory data discoverable, composable, and automated – we can build those experiences using AI models. when we bring these technologies together with an LLM, we can create intelligent AI-driven experiences. We can implement predictive and generative capabilities by using discoverable and consumable data via APIs.

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Generative AI (GenAI): Security

Generative AI (GenAI): Security

Generative artificial intelligence (generative AI) is a new buzzword across the industries. Generative AI is an artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.

All organizations are investing large amounts of their budget in GenAI technology. Recently Amazon completed a $4 billion investment in generative AI development. As per a recent study barely scratching the Generative AI use case and opportunity.

Before implementing any Generative AI solution make sure you completely understand the organization’s business problem to implement Gen AI solution, because any generative AI solution takes a lot of money, time, and brain power.

Evolution of LLMs

Generative AI has just blown up within the last year or two years, but it has been around for decades. Generative AI is based on large language models (LLM).  LLM has been evolving for a while technically five to ten years approx. All companies (like AWS, Microsoft, and Open AI) are presenting their standard based on their business requirements. Here is the evolution story of LLMs & GenAI.

AI Attacks

There are four types of AI attacks.

  1. Poisoning – This AI attack can lead to the loss of reputation and capital. This is a classic example of thrill-seekers and hacktivists injecting malicious content which subsequently disrupts the retraining process.
  2. Inference – This AI attack can result in the leakage of sensitive information. This attack aims to probe the machine learning model with different input data and weigh the output.
  3. Evasion – This AI attack can harm physical safety. This type of attack is usually carried out by Hacktivists aiming to get the product of a competitive company down and has the potential to seriously harm the physical safety of people.
  4. Extraction – This AI attack can lead to insider threats or cybercriminals. Based on this the attacker can extract the original model and create a stolen model to find evasion cases and fool the original model.

Type of AI Malware

  • Black Mamba – Black Mamba utilizes a benign executable that reaches out to a high-reputation API (OpenAI) at runtime, so it can return synthesized, malicious code needed to steal an infected user’s keystrokes. It has the below properties.
    • ChatGPT Polymorphic Malware
    • Dynamically Generates Code
    • Unique Malware code
  • Deep Locker – The Deep Locker class of malware stands in stark contrast to existing evasion techniques used by malware seen in the wild. It hides its malicious payload in benign carrier applications, such as video conference software, to avoid detection by most antivirus and malware scanners. It has the below properties.
    • Targeted identification
    • Logic detonation Mechanism
    • Facial and voice recognition
  • MalGAN – Generative Adversarial Networks serve as the foundation of Malware GAN and are used to create synthetic malware samples. For Mal-GAN’s complex design to function, it is made up of three essential parts: the generator, substitute detector, and malware detection system based on machine learning. It has the below properties.
    • Generative Adversarial Malware
    • Bypass ML-based Detections
    • Feed-forward Neural Networks

AI Security Threats

  • Deepfake Attacks
  • Mapping and Stealing AI Models
  • Spear Phishing (Deep Phishing)
  • Advanced Persistent Threats (APTs)
  • DDoS and Scanning of the Internet.
  • Data poisoning AI Models
  • PassGAN and MalGAN
  • Auto Generation of Exploit code
  • Ransom Negotiation Automation
  • Social Engineering

AI Security Defense Strategy

As we learned in AI several AI malware and threats are impacting different parts of the AI ecosystem. Our AI must be smart enough that it detects its threats and mitigates risk. ML-based malware detectors detect risk and generate insights into its severity. Here are a few approaches should implement to protect your AI systems.

  • Intelligent Automation
    • Automated response and Mitigation
    • Indicators of Compromise (IOCs) extraction and correlation
    • Behavioral and anomaly detection
  • Precision Approach
    • High Accuracy and Precision
    • Identify, Understand, and Neutralize
    • Prioritize Risk
  • Define the Area for defense
    • Identify the most vulnerable area.
    • Apply a broad spectrum of defense.
    • System resiliency

AI involvement in security

  • Malware detection – AI systems help prevent phishing, malware, and other malicious activities, ensuring a high-security posture and analyzing any unusual behavior.
  • Breach risk prediction – Identify the most vulnerable system and protect against any data leak.
  • Prioritize critical defense – AI-powered risk analysis can produce incident summaries for high-fidelity alerts and automate incident responses, accelerating alert investigations.
  • Correlating attack patterns – AI models can help balance security with user experience by analyzing the risk of each login attempt and verifying users through behavioral data, simplifying access for verified users
  • Adaptive response – AI model automated response and generate an alert if the system identifies any threats. This creates the first layer of security defense.
  • Applied Machine learning – AI models are self-train. If models identify any new risk pattern apply new security models to all protected systems.
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Zero Trust API Security Architect

The cybersecurity threat landscape has changed dramatically in the last couple of years. Every day new kinds of threats are coming and impacting the organization’s business. Infosec/Security teams have always had challenges with this new threat to find the root cause and mitigate these risks.

To mitigate and overcome these constant/real-time threats and risks, the security fraternity introduces Zero Trust Architecture (ZTA) Or Zero Trust Strategy (ZTS).  ZTA is not a product or application, but it is a concept and practice to mitigate any risk for your organization.

What is ZTA/ZTS?

Zero Trust is an information security model that denies access to applications and data by default. Threat prevention is achieved by continuously validating for security configuration and posture before being granted or keeping access to applications and data across users and their associated devices. All entities are untrusted by default; least privilege access is enforced; and comprehensive security monitoring is implemented.

Here are the basic properties for ZTA/ZTS

  • Default deny
  • Access by policy only
  • For data, workloads, users, devices
  • Least privilege access
  • Security monitoring
  • Risk-based verification

How API implement ZTA/ZTS?

API Security focuses on strategies and solutions to understand and mitigate the unique vulnerabilities and security risks of Application Programming Interfaces (APIs). In API security we establish certain rules and processes to mitigate security risks.  These rules and processes are around Zero trust architecture or strategy. Here are a few basic strategies in API security to implement ZTA.

  1. All API communications are secured regardless of network location – This risk can be mitigated by ensuring all communication happens over an encrypted communication channel (TLS) and implementing a proper Cross-Origin Resource Sharing (CORS) policy. The endpoint for API needs to be exposed through the HTTPS protocol.
  2. All API endpoints are authenticated regardless of their environments (Prod, QA, Dev) — By default, all APIs need to be authenticated and authorized using username/password, JSON Web Token (JWT), OAuth, OpenID Connect, or third-party services.
  3. All API resources are protected and restricted to all users by default — Running multiple versions of an API requires additional management resources from the API provider and expands the attack surface. As per ZTA, make sure all API versions and their resources are restricted if it is not used by the user. Always validate and properly sanitize data received from integrated APIs before using it.
  4. Access to API resources is determined by dynamic policy including the client identity, application/service, and the requesting asset – Any API requires resources such as network bandwidth, CPU, memory, and storage. It is easy to exploit these resources by simple API calls or multiple concurrent requests. According to Zero Trust Architect, all APIs must implement API policies like:
    • Client identity (ClientID/Client-Secret)
    • Execution timeouts (Rate limiting)
    • Maximum allowable memory
    • Maximum number of file descriptors
    • Maximum number of processes
    • Maximum upload file size
  5. Implement or configure API monitoring posture and API Alert system — API monitoring helps identify and resolve performance issues as well as security vulnerability issues before they negatively impact users, which can impact user experience. The alert system notifies the operation team to mitigate risk quickly.
  6. Continuous API security risk assessments – Continuous risk assessments help the Infosec/Security team identify any security risk gap. By conducting the security risk assessments, organizations establish a baseline of cybersecurity measurements, and such baselines could be referenced to or compared against future results to improve overall cyber posture and resiliency further and demonstrate progress. A Free Security assessments tool VAT is available to mitigate any security risk for your organization.

https://www.vanrish.com/secassessment/

Organizations that have adopted the Zero Trust API model, see trust as fundamental to creating a positive, low-friction work culture for their clients and empowering the organization at all levels. Many of our Vanrish Technology clients, we worked with have many of the technologies in place that can be leveraged toward full Zero Trust architect model adoption.

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What is Cybersecurity?

Everyday technological miracle is happening. New technologies are coming and impacting our lives and businesses.

The information technology (IT) trends of the past few years—the rise in cloud computing adoption, online banking, Online travel booking, driving connected car remote work & working from home, connecting with friends and family online, and connected devices and sensors in everything from doorbells to cars to assembly lines.

These conveniences could be a problem if you don’t have a well-protected system. Cyberattacks have the power to disrupt, damage, or destroy businesses and people’s lives. These cyber security risks can cause losses of billions of dollars to any organization. The average cost of a data breach in 2023 was USD 4.45 million, up 15 percent over the last years.

What is Cybersecurity?

Cybersecurity is the measure or practice for preventing cyberattacks and mitigating cyber risk by protecting internet-connected individuals’ and organizations’ systems such as hardware, software, and sensitive data.

Types of cybersecurity (cybersecurity domains)

Cybersecurity can be categorized into ten different types based on where it is impacting your systems.

  1. Application Security – Application security protects applications running on-premises and in the cloud, preventing unauthorized access to and use of applications and related data, and preventing flaws or vulnerabilities in application design that hackers can use to infiltrate the network. Modern application development methods
  2. Cloud Security – Cloud security secures an organization’s cloud-based services and assets—applications, data, storage, development tools, virtual servers, and cloud infrastructure. In most use cases, cloud security runs on the shared responsibility model. Cloud providers are responsible for securing the services they are providing, and the infrastructure they are delivering, while the customer’s responsibility is protecting their data, code, and other assets they store or run in the cloud.
  3. Data Security – Data security is the process of maintaining the confidentiality, integrity, and availability of digital information throughout its entire life cycle to protect it from corruption, theft, or unauthorized access.
  4. Identity and Access Control – Identity and Access control is a security technique that regulates who or what can view or use resources in a computing environment. There are two types of access control: physical and logical. Physical access control limits access to campuses, buildings, rooms, and physical IT assets. Logical access control limits connections to computer networks, system files, and data.
  5. Code Management – Code Management security comprises programming practices, techniques, and tools that ensure your code isn’t susceptible to security vulnerabilities. A hack or leak of source code can cause serious damage to a company on multiple fronts. It can harm the company’s reputation and lead to a loss of customer trust.
  6. Network Security – Network security is defined as the process of creating a strategic defensive approach that secures a company’s data and resources across its network. It prevents unauthorized access to network resources and detects and stops cyberattacks and network security breaches.
  7. Operations Security – Operations Security (OPSEC) is the process by which we protect critical information whether it is classified or unclassified that can be used against an organization. Things that fall under the OPSEC umbrella include monitoring behaviors and habits on social media sites as well as discouraging employees from sharing login credentials via email or text message.
  8. Physical and Environmental Security – Physical and environmental security refers to measures taken to protect systems, buildings, and related supporting infrastructure against threats associated with their physical environment.
  9. Mobile Security – Mobile security, often referred to as wireless security, involves protecting both personal and business-related information stored on and transmitted from smartphones, tablets, laptops, wearables, and other portable devices.
  10. Third-Party Relationships – Third-Party Relationships security includes security from external entities may include service providers, vendors, supply-side partners, demand-side partners, alliances, consortiums, and investors, and may include both contractual and non-contractual parties.

Get your Free Security Assessments to mitigate your risks.

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Generative AI for Public Sector: An API Opportunity

The disruptive power of AI extends to every industry, opening up unlimited possibilities for new business opportunities. It turns imagination into reality, insights into action, and possibility into discovery. Generative AI is a type of AI that produces content such as text, audio, code, videos, images, or any other content based on prompts input by the user. Generative AI models use complex computing processes like deep learning to analyze patterns from large sets of historical data to create new business opportunities.

Generative AI is a one of the most promising technologies that can help the public sector to improve productivity and service quality. However, it is important to ensure that the technology is used responsibly and ethically.

Generative AI can enable the public sector to improve productivity and service quality. Generative AI has a wide range of applications in the public sector. It can be used to extract information and automate paper-based processing. It can also be used to automate repetitive and mundane tasks, enabling staff to take on higher value work, optimize resource allocation, and enhance decision making. It also uses to summarize large amounts of information from different sources, such as public health data and economic indicators, to identify patterns, trends, and correlations for Government to take decision in favor public.

Here are a few examples of tasks that Generative AI can perform in the public sector:

  • Providing support to clients such as chatting, responding, and delegating task to correct department.
  • Writing and editing documents and emails
  • Coding tasks, such as debugging and generating templates and common solutions.
  • Summarizing information.
  • Research, translation, and learning

To ensure the responsible use of GenAI tools and maintain public trust , the public sector should align with the “FASTER” principles:

  • Fair: Content should comply with human rights, accessibility, procedural and unbiased obligations
  • Accountable: Content generated by these tools should make sure it is factual, legal, ethical, and compliant with the legal terms of use.
  • Secure: In pub-sec security is paramount goal. Content generated by Generative AI should appropriate for the security classification of the information and privacy & personal information are protected. Compliance with PII data.
  • Transparent: In Government sector, it is very important that your all procedural is transparent, and users know that they are interacting with an AI tool.
  • Educated: It is very important to document the strengths, limitations, and responsible use of the Generative AI tools. It should also highlight; how to create effective prompts and to identify potential weaknesses in the outputs.
  • Relevant: Generative AI tools should support user and organizational needs, contributes to improved outcomes and become relevant to society and business.

Since Generative AI has a wide range of benefits in the public sector, there are also some challenges associated with its use.

Here are Some of these challenges:

  1. Ethical dilemmas: Generative AI can be used to create deepfakes by manipulating videos and images. That can be used to spread misinformation and create confusion among public.
  2. Dependency on technology: Generative AI is dependent on the latest technology and underline system. It is based on how secure your data technology and how your data is communicating with AI models.
  3. Equity and accessibility issues: Generative automated certain task that led some job displacement. Which lead to accessibility and equity concern.
  4. Staff resistance to change: If Pub-Sec staff perceive Generative AI as a threat to their job then they may be resistance to change into Generative AI process.
  5. Project delays and failures: Generative AI projects are complex and time consuming. This may be delay or failure of project implementation.
  6. Regulatory issues: In Public Sector, data are fragmented which raises compliance and regulatory issue. This may be concerns about data privacy, security, and ownership.
  7. Cybersecurity risks: AI in the public sector raises cybersecurity risks. This may be concern about hacking, data breaches and other cyber threats.

API is helping GenAI to import the AI model and enable data for Generative AI. We can mitigate some of these risks by implementing API based approach for Generative AI in public sector.

Here are the few challenges in pub-sec Generative AI which is mitigated by API implementation.

  • Security: According to recent finding Generative AI makes it easier for hacker to find and exploit vulnerabilities. If your Generative AI models are communicating with your organization data through API, it will mitigate vulnerabilities risk many folds. Government sector can implement strict control of their data in a number of ways like MFA or API access permission.
  • Data control: Through API implementation in Generative AI, pub-sec can eliminate any data leakage and data abuse. Through API governance they can monitor data usage by Generative AI models. Government sector can also implement API rate limiting or IP restriction for any API to get tighter control on their sensitive data.
  • Fairness and relevancy:  Accuracy of Generative AI model or LLM are based on independent and relevancy of data. Generative AI models in pub-sec only work when Generative AI model follows compliances and relevant to use-case. API implementation does make sure data is relevant and independent for LLM. API also restrict any unwanted data for AI models and reduce processing time to cleansing unwanted data.   
  • Data Separation: APIs keep data separated from Generative AI Models or LLM (Large Language Model) implementation. This enable LLM to work on different set of data at the same time and enable faster innovation within government sector.
  • Fast delivery: APIs enable faster delivery of generative AI models. During your development of LLM models you focus only on models not on data deliveries. This may enable two stream of development team. One team focus only on data delivery and second team can focus only on Large language models development. This may empower to team for faster project deliveries.

Public sector adoption on Generative AI is still in the early stages, but it needs to accelerate. This will enable faster public project deliveries and AI bot assistances.

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Why Airlines Need Digital Transformation

When the Wright brothers flew their first plane they never imagined that after 100 years this industry will be one of the biggest and most complex. Initially Airlines were a part of luxury transportation but nowadays it is a necessity for most. Every day thousands of airplanes are flying and millions of passengers are reaching their destination.

To streamline the whole process, all departments of airlines should work synchronously and efficiently. There are a lot of variables involved to execute one task efficiently with a very little margin for error. The airline industry is the best example of machines and humans working together in harmony, which allows tasks to be completed quickly and accurately without any errors. The Airline’s biggest challenge is finding ways to reduce costs while still providing quality service.

Airline industries need to put forward the best process in place in order to remain competitive and profitable. All processes, including, operational structure, route network, fleet size, and pricing strategy need to be digitized and transparent to compete against their competitors and continue to be cost-effective.

Successful Adoption of Digital Transformation is the key to success to Airlines business.

Digital transformation allows Airlines to enable data efficiently and securely. It also helps in reducing the cost of operation and increases the efficiency.

Here are few area where Digital Transformation is helping Airlines to work efficiently and cost-effective .

Market & Partner Data – The Covid pandemic was a big disruption for the Airline market. Tracking and monitoring real-time Covid data after the pandemic is very important in managing your operation efficiently. Airlines work with their partner to get Market Data, Events, Weather, Traveler In-Flux, Reviews, and External Traveler Information for their operation. Integration with partner data is very important to get contextual insights for airlines.

Travel  Data – To run the airline industry efficiently their passenger System of Record needs to integrate efficiently. Those system are not limited to  Passenger service system (PSS),Computer reservation system ( CRS), Global distribution system (GDS), Enterprise Resource Planning (ERP), Traveler Profile, Fare, Schedule, Availability, Preferences, Assets, and Distribution with Offers & Orders (NDC).

Intent & Sentiment Data – Social Media Platform is one of the invaluable tools for airlines to stay ahead of their competitors. By leveraging Sentiment & Intent Behavior analysis on social media platforms, airlines can better understand passenger preferences and tailor services accordingly. This helps the Airlines build customer loyalty and increase profits over time.

Customer & Services Data – Managing historical customer and service data help airlines to get their customer sentiment and preference. These data includes Demographic and Identity Data, Profile, Cases Contact Center History, and Service Interactions data. This data helps airlines to understand passenger preference and provide better service. 

Marketing & Loyalty – Digital Transformation combining predictive analytics and human-centric design to create a more personalized experience to drive growth in loyalty, satisfaction and incremental revenue. It also helps in marketing to track Campaign Metrics, Digital Footprint, Experiential Targeting, Audience Segmentation, Digital Marketplace. 

Devices & Location Data – Airlines operation depends on IoT Sensor Data, Telemetry, Mobile, Voice, Geolocation, Location-Based data. This Intelligence based data is revolutionizing procurement through real time decision making. Allow the operational team to know the exact location of goods at any given time.

These are the big impact of digital transformation in airlines industries

  • Quicker time to market
  • Smooth Transitioning
  • Enhanced Business Agility
  • Reduce cost
  • Innovate and drive operational excellence   
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Security for Critical Data

When organization is migrating to digital transformation, data security is a big concern. Digital transformation impacts every aspect of business operation and execution. The volume of data that any organization creates, manipulates, and stores digitally is growing, and that drives a greater need for data governance. Large volume of data security is the biggest challenge for any organization for their entire data lifecycle. 

Data security is a process to protect sensitive data from unauthorized access, corruption, or theft  during the entire data lifecycle.

Here are a few steps to mitigate data risk and implement data security.

  • Event Monitoring
  • Data Detection
  • Data Encryption
  • Data Audit Trail

Event Monitoring – This activity includes Prevention, mitigation, and monitoring threats to sensitive data.

  • Monitor user activity – Know who is accessing data from where with real-time event streaming and min 3-6 months of event history.
  • Prevent and mitigate threats – Define and  build Transaction Security policies using declarative conditions or code to prevent and mitigate threats.
  • Drive adoption and performance – Analyze user behavior to enable security training for organization and find security bottlenecks to improve user experience.
  • Event Log Files – Create event log file for rich visibility into your org for security, adoption and performance purposes

Data Detection –  Find and classify the sensitive data quickly and mitigate data risk. 

  • Monitor Data Classification Coverage – Determine which data in your organization have been categorized versus uncategorized. High sensitive data needs to be secure properly. Label data appropriately to manage data security.

Data Encryption – Encrypt sensitive data at rest while preserving business functionality.

  • Encrypt data and maintain functionality – Protect data and attachments while data search, lookups, transportation and storage.
  • Key Management – Data encryption key management is very important to secure organization data. It includes control and authorization of data encryption keys.
  • Policy Management – Data policy management is defining and managing rules or procedures for accessing data. It allows individuals to follow certain processes to access data during storing or transit.. 

Data Audit Trail – It allows strengthening data integrity for an extended period. This strengthening data integrity process enables compliance and gains insights.

  • Data History – Store data as long as you can use this historical data for audit Trail or delete if you do not need this data.
  • Data retention policy – Data retention policy defines what data or how long this historical data needs to be stored for audit. Based on sensitivity of data you can archive from 3-6 months or more.
  • Insight of data – Create insight and dashboard for data audit transparency. It allows any organization to track any compliance or data security issue.
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RPA, BOTS, AI and API

In today’s competitive markets, industries face many challenges in order to remain successful. These include staying ahead of the competition, understanding customers need and preferences, and providing a high level of service that will make customers happy.

Here are few challenges for current industries

  1. Resolve customer issues ASAP
  2. Collect and qualify customer information
  3. Easily connect to business process
  4. Enable business new features quickly.

In current business requirements 90% of organizations see increased demand for automation from business teams, due to that 95% of IT leaders are prioritizing automation. 

Automation is a critical component of digital transformation and business success. Robotic Process Automation (RPA) bots are at the forefront of this revolution, providing businesses with an automated solution to optimize their processes while improving customer experiences. RPA bots can be used in many areas such as data entry, document processing and workflow management; they automate repetitive tasks that would otherwise take up valuable resources from human employees. This automation not only increases efficiency but also reduces costs associated with manual labor, allowing companies to focus on more pressing issues like innovation or collaboration between departments. By utilizing intelligent bots powered by artificial intelligence (AI), companies can further streamline operations and provide customers with immediate feedback on requests or inquiries in real time without manual intervention from employees. Additionally, natural language processing (NLP) capabilities allow chatbots used in websites or apps to respond quickly and accurately when communicating with customers.

Using NLP, Bots can decipher specific sentences or words customers type and associate them to an intent. NLP provides insights by analyzing past chat transcripts to identify common customer utterances or phrases (such as order status, account information, password reset, logging an issue, etc.) that the Bot can use to take action. A predictive model for bots to understand intent and take action called intent model. The intent model is made up of intents and utterances.

APIs , NLP and AI are the essential components for Bots. Once an intent model from NLP identifies action then Bots call APIs. APIs help to execute tasks from the backend system for Bots. Suppose if users are looking for order status from bots and APIs are not responding on time it will fail the whole Bots purpose. So APIs are one of the key components for Bots.

APIs streamline Bots tasks and automated any process/tasks for any team. Bots and APIs empower business and IT teams to collaborate with ease and break silos in every step of their automation journey. Enable end-to-end automation at scale Reuse and compose RPA securely.

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API Security

API is a key component of digital transformation. API is the interface of your legacy and SAAS data. The goal of APIs is to facilitate the transfer and enablement  of data between your system and external users. APIs are typically available through public networks like the internet to communicate to external users and expose your data into the public domain.

Since your data is exposed into the public domain through APIs, It can lead to a data breach. APIs can be broken and expose sensitive personal as well as company data. An insecure API can be an easy target for hackers to gain access to your system and network. Rise of IOT devices and usage of APIs by these IOT devices, APIs are now more vulnerable. 

According to owasp, these are 10 main API vulnerabilities.

  1. Broken Object Level Authorization – Expose endpoints that handle object identifiers, creating a wide attack surface Level Access Control issue.
  2. Broken User Authentication – Authentication mechanisms are implemented incorrectly.
  3. Excessive Data Exposure – Developers  expose all object properties without considering their individual sensitivity
  4. Lack of Resources & Rate Limiting – APIs do not impose any restrictions on the size or number of resources that can be requested by the client/user, lead to Denial of Service (DoS) attack on APIs
  5. Broken Function Level Authorization – Complex access control policies with different hierarchies lead to authorization flaws.
  6. Mass Assignment – Without proper properties filtering based on an allowlist, usually leads to Mass Assignment.
  7. Security Misconfiguration – Misconfiguration or lack of Security configuration  is commonly a result of insecure APIs
  8. SQL Injection – SQL Injection occurs when untrusted data is sent to an interpreter as part of a command or query.
  9. Improper Assets Management – APIs tend to expose more endpoints than traditional web applications lead to improper expose APIs.
  10. Insufficient Logging & Monitoring – Insufficient logging and monitoring fail to find your vulnerability and broken integration.

How to mitigate API security risk?

  • API supports secure sockets layer (SSL), transport layer security (TLS), and Hypertext Transfer Protocol Secure (HTTPS) protocols, which provide security by encrypting data during the transfer process.
  • Apply Basic Auth minimum with API or  if you want to more secure your API then enable 2 way authentication through OAuth framework . 
  • Apply Authorization on each API resource to more control on API security through external Identity and access management provider (IAM).
  • Use encryption and signatures to all your API exposed personal and organizational sensitive data.
  • Apply API throttling through API manager to control number of user access per API (Rate Limiting).
  • Implement best practice of exception handling on your APIs to hide all your internal server and database information to mitigate SQL injection security risk.
  • Use Service Mesh to manage different layers of API management and control.
  • Audit your APIs and remove all unused API from your API catalog.
  • Add proper logging, Monitoring and Alerting on your APIs to keep track of your APIs activity.

Conclusion: APIs are a critical part of modern AI, mobile, SaaS, IOT and web applications. APIs Security should be the main focus on strategies and solutions to mitigate the unique vulnerabilities and security risks .